From 35b421b9a201eff0c4a52f8f4595fa9d81c556fc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o?= Date: Wed, 6 Apr 2022 12:14:11 +0200 Subject: [PATCH 01/24] Add example on ensemble dimensions --- docs/examples/ensemble-dimensions.ipynb | 308 ++++++++++++++++++++++++ 1 file changed, 308 insertions(+) create mode 100644 docs/examples/ensemble-dimensions.ipynb diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb new file mode 100644 index 00000000..fc7d5c4a --- /dev/null +++ b/docs/examples/ensemble-dimensions.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ensemble dimensions: splitting changepoint detection algorithms along the dimensions\n", + "\n", + "\n", + "\n", + "## Introduction\n", + "\n", + "In `ruptures`, change point detection procedures make use of the same cost function along all the dimensions.\n", + "The choice of the cost function is critical as it is related to the type of change to find. \n", + "For instance, [CostL2](../user-guide/costs/costl2.md) can detect shifts in the mean, [CostNormal](../user-guide/costs/costnormal.md) can detect shifts in the mean and the covariance structure, [CostAR](../user-guide/costs/costautoregressive.md) can detect shifts in the auto-regressive structure, etc.\n", + "\n", + "However, in many settings, all the dimensions don't have the same type of changes and a single cost function is not able to spot all changes simultaneously.\n", + "To cope with this issue, a procedure to study the cost on each dimension independantly is presented here. It is inspired by the paper [[Katser2021]](#Katser2021), where a procedure to merge several cost functions has been introduced.\n", + "In a nutshell, different costs along each dimension can be combined to yield an aggregated cost function which is sensitive to several types of changes.\n", + "The aggregated cost can then be used with any search method (such as the [window search method](../user-guide/detection/window.md)) to create change point detection algorithm.\n", + "\n", + "This example illustrates the aggregation procedure, also referred to as an ensemble model.\n", + "Here, only [CostL2](../user-guide/costs/costl2.md) is considered for all dimensions, but all other costs could be used. The focus is then on the way the costs are combined (see [intersection](#intersection) and [union](#union)).\n", + "In addition, the number of changes is assumed to be known by the user." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First, we make the necessary imports and generate a multivariate toy signal which contains different mean shifts along the dimensions. Notice that only one changepoint is shared between the two dimensions. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from itertools import cycle\n", + "import matplotlib.pyplot as plt\n", + "import ruptures as rpt\n", + "\n", + "\n", + "# Scaling function\n", + "def minmax(array):\n", + " return (array - np.min(array, axis=0)) / (\n", + " np.max(array, axis=0) - np.min(array, axis=0) + 1e-8\n", + " )\n", + "\n", + "\n", + "# Aggregation functions\n", + "def min_(array):\n", + " return np.min(array, axis=1).T\n", + "\n", + "\n", + "def max_(array):\n", + " return np.max(array, axis=1).T" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bkps = [329, 656, 972, 1291, 1642, 2000]\n", + "n_samples = bkps[-1]\n", + "bkps_1 = [bkps[0], bkps[1], bkps[4], n_samples]\n", + "bkps_2 = [bkps[1], bkps[2], bkps[3], n_samples]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "signal = np.zeros((n_samples, 2))\n", + "val_cycle = cycle([0, 1])\n", + "for (start, end) in rpt.utils.pairwise([0] + bkps_1):\n", + " signal[start:end, 0] = next(val_cycle)\n", + "for (start, end) in rpt.utils.pairwise([0] + bkps_2):\n", + " signal[start:end, 1] = next(val_cycle)\n", + "\n", + "fig, axes = rpt.display(signal, bkps)\n", + "axes[0].set_title(\"Noise free signal\")\n", + "\n", + "signal += np.random.normal(size=signal.shape)\n", + "fig, axes = rpt.display(signal, bkps)\n", + "_ = axes[0].set_title(\"Toy signal\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell defines a custom cost that computes the [CostL2](../user-guide/costs/costl2.md) on the given dimension. To define a custom cost that would not compute the same cost on all dimensions, an `if` loop relying on the value of `self.dim` can be imagined. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from ruptures.base import BaseCost\n", + "\n", + "\n", + "class MyCost(BaseCost):\n", + "\n", + " \"\"\"Custom cost for exponential signals.\"\"\"\n", + "\n", + " # The 2 following attributes must be specified for compatibility.\n", + " model = \"\"\n", + " min_size = 2\n", + "\n", + " def __init__(self, dim):\n", + " super().__init__()\n", + " self.dim = dim\n", + "\n", + " def fit(self, signal):\n", + " \"\"\"Set the internal parameter.\"\"\"\n", + " self.signal = signal[:, self.dim].reshape(-1, 1)\n", + " return self\n", + "\n", + " def error(self, start, end) -> float:\n", + " \"\"\"Return the approximation cost on the segment [start:end].\n", + "\n", + " Args:\n", + " start (int): start of the segment\n", + " end (int): end of the segment\n", + "\n", + " Returns:\n", + " segment cost\n", + "\n", + " Raises:\n", + " NotEnoughPoints: when the segment is too short (less than `min_size` samples).\n", + " \"\"\"\n", + " if end - start < self.min_size:\n", + " raise rpt.exceptions.NotEnoughPoints\n", + "\n", + " return self.signal[start:end].var(axis=0).sum() * (end - start)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Looking at the costs along each dimension\n", + "\n", + "Here, the [window search method](../user-guide/detection/window.md) is used. Thus, the scores will be considered instead of the costs.\n", + "\n", + "The following cell shows the scores along each dimension:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "window_size = 200\n", + "\n", + "list_of_costs = [MyCost(dim=0), MyCost(dim=1)]\n", + "\n", + "scores = []\n", + "\n", + "for cost in list_of_costs:\n", + " algo = rpt.Window(width=window_size, custom_cost=cost, jump=1).fit(signal)\n", + " scores.append(algo.score)\n", + "scores = np.array(scores).T\n", + "\n", + "# For display purpose\n", + "appended_scores = np.append(\n", + " np.ones((window_size // 2, 2)) * float(\"inf\"), scores, axis=0\n", + ")\n", + "fig, axes = rpt.display(appended_scores, bkps)\n", + "_ = axes[0].set_title(\"Scores along each dimension\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intersection\n", + "\n", + "A first way of aggregating the scores is to consider each score along a dimension as being an expert. The idea is then to take the __intersection__ of the experts so that the predicted changepoints are the changepoints where all the experts are confident. This method is useful when the user is interested in collecting the changepoints that have been detected on all dimensions at the same timestep.\n", + "\n", + "In the following cell, the intersection procedure correctly predicts the only common changepoint between the two dimensions. The aggregated score shows clearly the only changepoint to predict." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bkps_intersection = [bkps[1], n_samples]\n", + "n_bkps_intersection = 1\n", + "\n", + "# intersection aggregation\n", + "algo.score = min_(minmax(scores))\n", + "bkps_intersection_predicted = algo.predict(n_bkps=n_bkps_intersection)\n", + "\n", + "# For display purpose\n", + "appended_intersection_scores = np.append(\n", + " np.ones(window_size // 2) * float(\"inf\"), algo.score\n", + ")\n", + "\n", + "fig, (ax,) = rpt.display(\n", + " appended_intersection_scores, bkps_intersection, bkps_intersection_predicted\n", + ")\n", + "_ = ax.set_title(\"Aggregated score for intersection purpose\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Union\n", + "\n", + "Another way of aggregating the scores is to take the __union__ of the experts so that the predicted changepoints are the changepoints where at least one expert is confident. This method is useful when the user is interested in collecting all the changepoints that are present along all dimensions.\n", + "\n", + "In the following cell, the union procedure correctly predicts all the changepoints. The aggregated score shows 5 clear peaks from which to take the changepoints." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bkps_union = bkps\n", + "n_bkps_union = 5\n", + "\n", + "# union aggregation\n", + "algo.score = max_(minmax(scores))\n", + "bkps_union_predicted = algo.predict(n_bkps=n_bkps_union)\n", + "\n", + "# For display purpose\n", + "appended_union_scores = np.append(np.ones(window_size // 2) * float(\"inf\"), algo.score)\n", + "\n", + "fig, (ax,) = rpt.display(appended_union_scores, bkps_union, bkps_union_predicted)\n", + "_ = ax.set_title(\"Aggregated score for union purpose\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This example shows a way of crafting a changepoint detection algorithm at the scale of the dimensions. This is a finer scale than the usual one: the signal scale.\n", + "\n", + "Two options are presented: [intersection](#intersection) where a changepoint is detected only if it is detected along all dimensions and [union](#union) where a changepoint is detected as soon as one dimension has detected it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Authors\n", + "\n", + "This example notebook has been authored by [Théo VINCENT](https://github.com/theovincent) and edited by [Olivier Boulant](https://github.com/oboulant) and [Charles Truong](https://github.com/deepcharles)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[Katser2021]\n", + "Katser, I., Kozitsin, V., Lobachev, V., & Maksimov, I. (2021). Unsupervised Offline Changepoint Detection Ensembles. Applied Sciences, 11(9), 4280." + ] + } + ], + "metadata": { + "interpreter": { + "hash": "de7c5fd504cc39d35018b8f7c7b90638a48a11123680d0b8595c478c27913e5d" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 5ef3879155a5b4d4d679e968829bdfe92c8cf4ab Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o?= Date: Wed, 6 Apr 2022 12:15:33 +0200 Subject: [PATCH 02/24] Add example in docs --- mkdocs.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mkdocs.yml b/mkdocs.yml index c319f548..ce86bea6 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -77,9 +77,11 @@ nav: - 'Simple usages': - 'Basic usage': examples/basic-usage.ipynb - 'Advanced usages': + - 'Dimensions ensemble model': examples/ensemble-dimensions.ipynb - 'Kernel change point detection: a performance comparison': examples/kernel-cpd-performance-comparison.ipynb - 'Music segmentation': examples/music-segmentation.ipynb - 'Text segmentation': examples/text-segmentation.ipynb + - Code reference: - code-reference/index.md - Base classes: code-reference/base-reference.md From 174100f9ac64e4465bf01ec2e77c0a7c76bb15b1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:49:35 +0200 Subject: [PATCH 03/24] Update docs/examples/ensemble-dimensions.ipynb spelling mistake Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index fc7d5c4a..1df1178d 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -11,7 +11,7 @@ "## Introduction\n", "\n", "In `ruptures`, change point detection procedures make use of the same cost function along all the dimensions.\n", - "The choice of the cost function is critical as it is related to the type of change to find. \n", + "The choice of the cost function is critical as it is related to the type of changes to find. \n", "For instance, [CostL2](../user-guide/costs/costl2.md) can detect shifts in the mean, [CostNormal](../user-guide/costs/costnormal.md) can detect shifts in the mean and the covariance structure, [CostAR](../user-guide/costs/costautoregressive.md) can detect shifts in the auto-regressive structure, etc.\n", "\n", "However, in many settings, all the dimensions don't have the same type of changes and a single cost function is not able to spot all changes simultaneously.\n", From fa6be644a8e3000b8fee4bcd2cabf6cd5bf8ce57 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:50:16 +0200 Subject: [PATCH 04/24] Update docs/examples/ensemble-dimensions.ipynb formulation Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 1df1178d..2660ab7e 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -15,7 +15,7 @@ "For instance, [CostL2](../user-guide/costs/costl2.md) can detect shifts in the mean, [CostNormal](../user-guide/costs/costnormal.md) can detect shifts in the mean and the covariance structure, [CostAR](../user-guide/costs/costautoregressive.md) can detect shifts in the auto-regressive structure, etc.\n", "\n", "However, in many settings, all the dimensions don't have the same type of changes and a single cost function is not able to spot all changes simultaneously.\n", - "To cope with this issue, a procedure to study the cost on each dimension independantly is presented here. It is inspired by the paper [[Katser2021]](#Katser2021), where a procedure to merge several cost functions has been introduced.\n", + "To cope with this issue, we present here a procedure to study the cost on each dimension independantly. It is inspired by [[Katser2021]](#Katser2021), where a procedure to merge several cost functions has been introduced.\n", "In a nutshell, different costs along each dimension can be combined to yield an aggregated cost function which is sensitive to several types of changes.\n", "The aggregated cost can then be used with any search method (such as the [window search method](../user-guide/detection/window.md)) to create change point detection algorithm.\n", "\n", From d245af4e7a3423a5d297883c7e5b7a5c9367654d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:50:43 +0200 Subject: [PATCH 05/24] Update docs/examples/ensemble-dimensions.ipynb Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 2660ab7e..bcc58889 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -44,8 +44,9 @@ "import matplotlib.pyplot as plt\n", "import ruptures as rpt\n", "\n", - "\n", + "###############\n", "# Scaling function\n", + "###############\n" "def minmax(array):\n", " return (array - np.min(array, axis=0)) / (\n", " np.max(array, axis=0) - np.min(array, axis=0) + 1e-8\n", From e40ad64f86d3f4efb120cc49f847cc8fd064d891 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:51:14 +0200 Subject: [PATCH 06/24] Update docs/examples/ensemble-dimensions.ipynb Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index bcc58889..8f75df1c 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -52,8 +52,9 @@ " np.max(array, axis=0) - np.min(array, axis=0) + 1e-8\n", " )\n", "\n", - "\n", + "###############\n", "# Aggregation functions\n", + "###############\n", "def min_(array):\n", " return np.min(array, axis=1).T\n", "\n", From 2c196cf3192cac7f7780a6510fe347babb2f37ca Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:51:24 +0200 Subject: [PATCH 07/24] Update docs/examples/ensemble-dimensions.ipynb Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 8f75df1c..0312507f 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -58,7 +58,6 @@ "def min_(array):\n", " return np.min(array, axis=1).T\n", "\n", - "\n", "def max_(array):\n", " return np.max(array, axis=1).T" ] From ce3136c26da4f2fe32ce07a546b67e4f81334391 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:51:42 +0200 Subject: [PATCH 08/24] Update docs/examples/ensemble-dimensions.ipynb Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 0312507f..c1cc375e 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -68,6 +68,7 @@ "metadata": {}, "outputs": [], "source": [ + "# Create change points for both dimensions\n", "bkps = [329, 656, 972, 1291, 1642, 2000]\n", "n_samples = bkps[-1]\n", "bkps_1 = [bkps[0], bkps[1], bkps[4], n_samples]\n", From 358738c6c8e50358d1c8591aa51258b971ad4dd4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:51:58 +0200 Subject: [PATCH 09/24] Update docs/examples/ensemble-dimensions.ipynb Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index c1cc375e..82294643 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -81,6 +81,7 @@ "metadata": {}, "outputs": [], "source": [ + "# Generate both signals given respective change points \n", "signal = np.zeros((n_samples, 2))\n", "val_cycle = cycle([0, 1])\n", "for (start, end) in rpt.utils.pairwise([0] + bkps_1):\n", From e66e45b7bd8ec3f479c64be8709c4f629ec5dff2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:52:05 +0200 Subject: [PATCH 10/24] Update docs/examples/ensemble-dimensions.ipynb Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 82294643..79d31918 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -92,6 +92,7 @@ "fig, axes = rpt.display(signal, bkps)\n", "axes[0].set_title(\"Noise free signal\")\n", "\n", + "# Add noise\n", "signal += np.random.normal(size=signal.shape)\n", "fig, axes = rpt.display(signal, bkps)\n", "_ = axes[0].set_title(\"Toy signal\")" From a73d44bc42131a475e1e38de487d1c0cb07054e9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:52:31 +0200 Subject: [PATCH 11/24] Update docs/examples/ensemble-dimensions.ipynb Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 79d31918..ea4824c7 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -102,7 +102,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following cell defines a custom cost that computes the [CostL2](../user-guide/costs/costl2.md) on the given dimension. To define a custom cost that would not compute the same cost on all dimensions, an `if` loop relying on the value of `self.dim` can be imagined. " + "The following cell defines a [custom cost](../user-guide/costs/costcustom.md) that computes the [CostL2](../user-guide/costs/costl2.md) on the given dimension. To define a custom cost that would not compute the same cost on all dimensions, an `if` loop relying on the value of `self.dim` can be imagined. " ] }, { From 1c4918ba5568948afb229b96d0ab9ca12359642c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:52:38 +0200 Subject: [PATCH 12/24] Update docs/examples/ensemble-dimensions.ipynb Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index ea4824c7..21a1d256 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -115,7 +115,6 @@ "source": [ "from ruptures.base import BaseCost\n", "\n", - "\n", "class MyCost(BaseCost):\n", "\n", " \"\"\"Custom cost for exponential signals.\"\"\"\n", From 885c28be60b8766e4308b0bb6bb5bace0a27d271 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:55:59 +0200 Subject: [PATCH 13/24] Update docs/examples/ensemble-dimensions.ipynb Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 21a1d256..4019b6ca 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -117,7 +117,6 @@ "\n", "class MyCost(BaseCost):\n", "\n", - " \"\"\"Custom cost for exponential signals.\"\"\"\n", "\n", " # The 2 following attributes must be specified for compatibility.\n", " model = \"\"\n", From 5ce4705b8b556f2bc30933ff040d8951829adc47 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20VINCENT?= <49516344+theovincent@users.noreply.github.com> Date: Wed, 13 Apr 2022 16:56:33 +0200 Subject: [PATCH 14/24] Update docs/examples/ensemble-dimensions.ipynb Co-authored-by: Olivier Boulant --- docs/examples/ensemble-dimensions.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 4019b6ca..d8d006db 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -258,7 +258,7 @@ "source": [ "## Conclusion\n", "\n", - "This example shows a way of crafting a changepoint detection algorithm at the scale of the dimensions. This is a finer scale than the usual one: the signal scale.\n", + "Rather than using \"regular\" cost functions that needs to aggregate all dimensions in the first place, this example introduces a way of crafting a changepoint detection algorithm based on a smaller dimension level.\n", "\n", "Two options are presented: [intersection](#intersection) where a changepoint is detected only if it is detected along all dimensions and [union](#union) where a changepoint is detected as soon as one dimension has detected it." ] From 3c3ed5c55be7ab3715ee8ddf2cbe4875bd61a700 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o?= Date: Wed, 13 Apr 2022 17:19:08 +0200 Subject: [PATCH 15/24] Fix bug comma (ensemble-dimensions) --- docs/examples/ensemble-dimensions.ipynb | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index d8d006db..87c03951 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -46,18 +46,20 @@ "\n", "###############\n", "# Scaling function\n", - "###############\n" + "###############\n", "def minmax(array):\n", " return (array - np.min(array, axis=0)) / (\n", " np.max(array, axis=0) - np.min(array, axis=0) + 1e-8\n", " )\n", "\n", + "\n", "###############\n", "# Aggregation functions\n", "###############\n", "def min_(array):\n", " return np.min(array, axis=1).T\n", "\n", + "\n", "def max_(array):\n", " return np.max(array, axis=1).T" ] @@ -81,7 +83,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Generate both signals given respective change points \n", + "# Generate both signals given respective change points\n", "signal = np.zeros((n_samples, 2))\n", "val_cycle = cycle([0, 1])\n", "for (start, end) in rpt.utils.pairwise([0] + bkps_1):\n", @@ -115,8 +117,8 @@ "source": [ "from ruptures.base import BaseCost\n", "\n", - "class MyCost(BaseCost):\n", "\n", + "class MyCost(BaseCost):\n", "\n", " # The 2 following attributes must be specified for compatibility.\n", " model = \"\"\n", From 3ad84d5a1e28bf5fff81b3307f9e0758c606ee61 Mon Sep 17 00:00:00 2001 From: Charles T Date: Wed, 13 Apr 2022 22:01:58 +0200 Subject: [PATCH 16/24] docs: add corrections to notebook --- docs/examples/ensemble-dimensions.ipynb | 751 ++++++++++++++---------- 1 file changed, 441 insertions(+), 310 deletions(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 87c03951..db17b240 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -1,312 +1,443 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Ensemble dimensions: splitting changepoint detection algorithms along the dimensions\n", - "\n", - "\n", - "\n", - "## Introduction\n", - "\n", - "In `ruptures`, change point detection procedures make use of the same cost function along all the dimensions.\n", - "The choice of the cost function is critical as it is related to the type of changes to find. \n", - "For instance, [CostL2](../user-guide/costs/costl2.md) can detect shifts in the mean, [CostNormal](../user-guide/costs/costnormal.md) can detect shifts in the mean and the covariance structure, [CostAR](../user-guide/costs/costautoregressive.md) can detect shifts in the auto-regressive structure, etc.\n", - "\n", - "However, in many settings, all the dimensions don't have the same type of changes and a single cost function is not able to spot all changes simultaneously.\n", - "To cope with this issue, we present here a procedure to study the cost on each dimension independantly. It is inspired by [[Katser2021]](#Katser2021), where a procedure to merge several cost functions has been introduced.\n", - "In a nutshell, different costs along each dimension can be combined to yield an aggregated cost function which is sensitive to several types of changes.\n", - "The aggregated cost can then be used with any search method (such as the [window search method](../user-guide/detection/window.md)) to create change point detection algorithm.\n", - "\n", - "This example illustrates the aggregation procedure, also referred to as an ensemble model.\n", - "Here, only [CostL2](../user-guide/costs/costl2.md) is considered for all dimensions, but all other costs could be used. The focus is then on the way the costs are combined (see [intersection](#intersection) and [union](#union)).\n", - "In addition, the number of changes is assumed to be known by the user." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First, we make the necessary imports and generate a multivariate toy signal which contains different mean shifts along the dimensions. Notice that only one changepoint is shared between the two dimensions. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from itertools import cycle\n", - "import matplotlib.pyplot as plt\n", - "import ruptures as rpt\n", - "\n", - "###############\n", - "# Scaling function\n", - "###############\n", - "def minmax(array):\n", - " return (array - np.min(array, axis=0)) / (\n", - " np.max(array, axis=0) - np.min(array, axis=0) + 1e-8\n", - " )\n", - "\n", - "\n", - "###############\n", - "# Aggregation functions\n", - "###############\n", - "def min_(array):\n", - " return np.min(array, axis=1).T\n", - "\n", - "\n", - "def max_(array):\n", - " return np.max(array, axis=1).T" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create change points for both dimensions\n", - "bkps = [329, 656, 972, 1291, 1642, 2000]\n", - "n_samples = bkps[-1]\n", - "bkps_1 = [bkps[0], bkps[1], bkps[4], n_samples]\n", - "bkps_2 = [bkps[1], bkps[2], bkps[3], n_samples]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate both signals given respective change points\n", - "signal = np.zeros((n_samples, 2))\n", - "val_cycle = cycle([0, 1])\n", - "for (start, end) in rpt.utils.pairwise([0] + bkps_1):\n", - " signal[start:end, 0] = next(val_cycle)\n", - "for (start, end) in rpt.utils.pairwise([0] + bkps_2):\n", - " signal[start:end, 1] = next(val_cycle)\n", - "\n", - "fig, axes = rpt.display(signal, bkps)\n", - "axes[0].set_title(\"Noise free signal\")\n", - "\n", - "# Add noise\n", - "signal += np.random.normal(size=signal.shape)\n", - "fig, axes = rpt.display(signal, bkps)\n", - "_ = axes[0].set_title(\"Toy signal\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cell defines a [custom cost](../user-guide/costs/costcustom.md) that computes the [CostL2](../user-guide/costs/costl2.md) on the given dimension. To define a custom cost that would not compute the same cost on all dimensions, an `if` loop relying on the value of `self.dim` can be imagined. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from ruptures.base import BaseCost\n", - "\n", - "\n", - "class MyCost(BaseCost):\n", - "\n", - " # The 2 following attributes must be specified for compatibility.\n", - " model = \"\"\n", - " min_size = 2\n", - "\n", - " def __init__(self, dim):\n", - " super().__init__()\n", - " self.dim = dim\n", - "\n", - " def fit(self, signal):\n", - " \"\"\"Set the internal parameter.\"\"\"\n", - " self.signal = signal[:, self.dim].reshape(-1, 1)\n", - " return self\n", - "\n", - " def error(self, start, end) -> float:\n", - " \"\"\"Return the approximation cost on the segment [start:end].\n", - "\n", - " Args:\n", - " start (int): start of the segment\n", - " end (int): end of the segment\n", - "\n", - " Returns:\n", - " segment cost\n", - "\n", - " Raises:\n", - " NotEnoughPoints: when the segment is too short (less than `min_size` samples).\n", - " \"\"\"\n", - " if end - start < self.min_size:\n", - " raise rpt.exceptions.NotEnoughPoints\n", - "\n", - " return self.signal[start:end].var(axis=0).sum() * (end - start)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Looking at the costs along each dimension\n", - "\n", - "Here, the [window search method](../user-guide/detection/window.md) is used. Thus, the scores will be considered instead of the costs.\n", - "\n", - "The following cell shows the scores along each dimension:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "window_size = 200\n", - "\n", - "list_of_costs = [MyCost(dim=0), MyCost(dim=1)]\n", - "\n", - "scores = []\n", - "\n", - "for cost in list_of_costs:\n", - " algo = rpt.Window(width=window_size, custom_cost=cost, jump=1).fit(signal)\n", - " scores.append(algo.score)\n", - "scores = np.array(scores).T\n", - "\n", - "# For display purpose\n", - "appended_scores = np.append(\n", - " np.ones((window_size // 2, 2)) * float(\"inf\"), scores, axis=0\n", - ")\n", - "fig, axes = rpt.display(appended_scores, bkps)\n", - "_ = axes[0].set_title(\"Scores along each dimension\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Intersection\n", - "\n", - "A first way of aggregating the scores is to consider each score along a dimension as being an expert. The idea is then to take the __intersection__ of the experts so that the predicted changepoints are the changepoints where all the experts are confident. This method is useful when the user is interested in collecting the changepoints that have been detected on all dimensions at the same timestep.\n", - "\n", - "In the following cell, the intersection procedure correctly predicts the only common changepoint between the two dimensions. The aggregated score shows clearly the only changepoint to predict." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bkps_intersection = [bkps[1], n_samples]\n", - "n_bkps_intersection = 1\n", - "\n", - "# intersection aggregation\n", - "algo.score = min_(minmax(scores))\n", - "bkps_intersection_predicted = algo.predict(n_bkps=n_bkps_intersection)\n", - "\n", - "# For display purpose\n", - "appended_intersection_scores = np.append(\n", - " np.ones(window_size // 2) * float(\"inf\"), algo.score\n", - ")\n", - "\n", - "fig, (ax,) = rpt.display(\n", - " appended_intersection_scores, bkps_intersection, bkps_intersection_predicted\n", - ")\n", - "_ = ax.set_title(\"Aggregated score for intersection purpose\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Union\n", - "\n", - "Another way of aggregating the scores is to take the __union__ of the experts so that the predicted changepoints are the changepoints where at least one expert is confident. This method is useful when the user is interested in collecting all the changepoints that are present along all dimensions.\n", - "\n", - "In the following cell, the union procedure correctly predicts all the changepoints. The aggregated score shows 5 clear peaks from which to take the changepoints." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bkps_union = bkps\n", - "n_bkps_union = 5\n", - "\n", - "# union aggregation\n", - "algo.score = max_(minmax(scores))\n", - "bkps_union_predicted = algo.predict(n_bkps=n_bkps_union)\n", - "\n", - "# For display purpose\n", - "appended_union_scores = np.append(np.ones(window_size // 2) * float(\"inf\"), algo.score)\n", - "\n", - "fig, (ax,) = rpt.display(appended_union_scores, bkps_union, bkps_union_predicted)\n", - "_ = ax.set_title(\"Aggregated score for union purpose\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "Rather than using \"regular\" cost functions that needs to aggregate all dimensions in the first place, this example introduces a way of crafting a changepoint detection algorithm based on a smaller dimension level.\n", - "\n", - "Two options are presented: [intersection](#intersection) where a changepoint is detected only if it is detected along all dimensions and [union](#union) where a changepoint is detected as soon as one dimension has detected it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Authors\n", - "\n", - "This example notebook has been authored by [Théo VINCENT](https://github.com/theovincent) and edited by [Olivier Boulant](https://github.com/oboulant) and [Charles Truong](https://github.com/deepcharles)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "[Katser2021]\n", - "Katser, I., Kozitsin, V., Lobachev, V., & Maksimov, I. (2021). Unsupervised Offline Changepoint Detection Ensembles. Applied Sciences, 11(9), 4280." - ] - } - ], - "metadata": { - "interpreter": { - "hash": "de7c5fd504cc39d35018b8f7c7b90638a48a11123680d0b8595c478c27913e5d" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Combining cost functions\n", + "\n", + "\n", + "\n", + "## Introduction\n", + "\n", + "In `ruptures`, change point detection procedures can only use a single cost function.\n", + "The choice of this cost function is crucial as it is related to the type of change to find. \n", + "For instance, [CostL2](../user-guide/costs/costl2.md) can detect shifts in the mean, [CostNormal](../user-guide/costs/costnormal.md) can detect shifts in the mean and the covariance structure, [CostAR](../user-guide/costs/costautoregressive.md) can detect shifts in the auto-regressive structure, etc.\n", + "\n", + "However, in many settings, several types of changes exist within a signal and a single cost function is not able to detect all changes simultaneously.\n", + "To cope with this issue, a procedure to combine cost functions is needed.\n", + "In this examples, a technique inspired by [[Katser2021]](#Katser2021) is presented.\n", + "In a nutshell, different costs are combined to yield an aggregated cost function which is sensitive to several types of changes.\n", + "The aggregated cost can then be used with the [window search method](../user-guide/detection/window.md) to create a change point detection algorithm.\n", + "\n", + "For simplicity, we focus on mean-shifts that occurr in different dimensions of a multivariate signal.\n", + "Each considered cost function can only detect mean-shift in a single dimension.\n", + "The user can choose between two aggregations procedures that can:\n", + "\n", + "- either detect shifts that occur simultaneously on all dimensions (intersection of experts),\n", + "- or detect shifts that occur on any dimension (union of experts).\n", + "\n", + "Here, only [CostL2](../user-guide/costs/costl2.md) is considered for all dimensions, but all other costs could be used. The focus is then on the way the costs are combined (see [intersection](#intersection) and [union](#union)).\n", + "In addition, the number of changes is assumed to be known by the user." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First, we make the necessary imports and a few define utility functions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import ruptures as rpt\n", + "from ruptures.base import BaseCost\n", + "\n", + "WINDOW_SIZE = 200\n", + "\n", + "def minmax_scale(array: np.ndarray)->np.ndarray:\n", + " \"\"\"Scale each dimension to the [0, 1] range.\"\"\"\n", + " return (array - np.min(array, axis=0)) / (\n", + " np.max(array, axis=0) - np.min(array, axis=0) + 1e-8\n", + " )\n", + "\n", + "\n", + "def fig_ax(figsize=(10, 3)):\n", + " return plt.subplots(figsize=figsize)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The merging procedure of cost functions is applied on the following 2D toy signal, where each dimension contains three mean-shifts.\n", + "Note that only one mean-shift is shared by both dimensions and all other changes occur at different indexes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]])\n", + "bkps_on_dim_0 = [329, 656, 1642, 2000]\n", + "bkps_on_dim_1 = [656, 972, 1291, 2000]\n", + "bkps_all_dims = [329, 656, 972, 1291, 1642, 2000]\n", + "\n", + "signal_with_noise = signal_no_noise + np.random.normal(size=signal_no_noise.shape)\n", + "\n", + "fig, axes = rpt.display(signal_no_noise, bkps_all_dims)\n", + "_ = axes[0].set_title(\"Toy signal (no noise)\")\n", + "\n", + "fig, axes = rpt.display(signal_with_noise, bkps_all_dims)\n", + "_ = axes[0].set_title(\"Toy signal (with noise)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Detection with a single cost function\n", + "\n", + "Consider the following cost function (derived from [CostL2](../user-guide/costs/costl2.md)) that can only detect mean-shift on one dimension at a time." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class CostL2OnSingleDim(BaseCost):\n", + " \"\"\"This cost function detects mean-shift on a single dimension of a multivariate signal.\"\"\"\n", + "\n", + " # The 2 following attributes must be specified for compatibility.\n", + " model = \"CostL2OnSingleDim\"\n", + " min_size = 1\n", + "\n", + " def __init__(self, dim):\n", + " super().__init__()\n", + " self.dim = dim\n", + "\n", + " def fit(self, signal):\n", + " \"\"\"Set the internal parameter.\"\"\"\n", + " self.signal = signal[:, self.dim].reshape(-1, 1)\n", + " return self\n", + "\n", + " def error(self, start, end) -> float:\n", + " \"\"\"Return the approximation cost on the segment [start:end].\n", + "\n", + " Args:\n", + " start (int): start of the segment\n", + " end (int): end of the segment\n", + "\n", + " Returns:\n", + " segment cost\n", + "\n", + " Raises:\n", + " NotEnoughPoints: when the segment is too short (less than `min_size` samples).\n", + " \"\"\"\n", + " if end - start < self.min_size:\n", + " raise rpt.exceptions.NotEnoughPoints\n", + " if end - start == 1:\n", + " return 0.\n", + " return self.signal[start:end].var(axis=0).sum() * (end - start)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Combining this cost function with the [window search method](../user-guide/detection/window.md) yields the following change-points.\n", + "Note that the true changes are shown with the alternating colors and the estimated changes are shown with the vertical dashed lines." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Detect mean-shift on the first dimension\n", + "cost_function = CostL2OnSingleDim(dim=0)\n", + "algo_on_dim_0 = rpt.Window(width=WINDOW_SIZE, custom_cost=cost_function, jump=1).fit(signal_with_noise)\n", + "bkps_pred = algo_on_dim_0.predict(n_bkps=len(bkps_on_dim_0)-1) # the number of changes is known\n", + "# display signal and changes\n", + "fig, axes = rpt.display(signal_with_noise, bkps_on_dim_0, bkps_pred)\n", + "_ = axes[0].set_title((f\"\"\"Detection of mean-shifts using only Dimension 0:\\n\"\"\"\n", + "f\"\"\"true changes: {bkps_on_dim_0[:-1]}, detected changes: {bkps_pred[:-1]}.\"\"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wfzqAIdr3BMCLAEq0a9cBGOfw/ervSHhPeQBOtzg/5D2bjn8O4LEIf+sVAH4vfL4GQJ7FuTcC2AnN+VZy/HAAtRHU4VMAj4Q55zIA67W/s7W22Es4/g6ATyTXLQBwk0WZFMAIh3W8HsAerR0/7LSda32FgvWjOgDH2rUr7dhYAL+CJYEoBvAPAOcAaAHQqpWzVjuXCx6FYKmJHwfg1Y55ATwHtojaCeAurS4pFs84CMB3AEq153hN+/4m7T0+p9V3F1jabn7dzdqz1Gr3ucPcdsGSeJRo9bxZON4DTOCrARO2HgewQDh+kPAucgFcIRw7D8Am7b77ANzv8Ld8BCxzo/jdk7L2Y9Vnbc4jWju40WFd7gbwi/DZAzbuhowFALK0Zz3ZoqxR2vu9wsm9hevOALDbwXk1AI7Q/r4SwDJT3ShYem5H/RNsbH7cTV3Vv475T2l+FW0CZdvxexHUdDwjHD4ZTBN3tvRiI38GcIl2TX+wifN12YmEkKPAsj39DUwDchLYhMO5BmyS7Q0gDcD9wrGpYNmZeoNp+szbaVcDeBRMU7kdwBPaPXsC+AYs1WwPsMn1OKFOl4AJHpcB6AVgPpjmBWBC80lg2tIcsMmgXLvuGkLIOquXImGg9m+cZrKwixDyKCFE7PO9ta3YXZqZgmireIx23/XaFuinhKUIdgLR/omfBxJjmmbOjQA+ppRahZ05CSwDWjwQyyam//nf4+JxY02z/iaYANwfrK0MFE6xa+cnaf/naH1psV270kw7ZoJpIfuDaSB/o5ROAxPQ+HY9zwb4EVgWtxEADgNrl3zr+w9gOwiHgWn3L7d5Ri9Y1rk9YAvdATCmOj4arH/0BPAMgImEEP7+S7T7dAHroy8SQg4Xru0LJqQPAFs8v04I6aYdex1MA9kXrH3p5jxaG/8VbHHXG6wfv0EIGaudMhFM0O4M9tvPEq6tErfhHXAMgArN7KCEEDKZEDLYdM5nhJn3zCCEHCIrBCxLWx8A3zq8r6z/WbXl34EtTOYZCmCmSg0AtoAJv1Mc3tsxhKVeTwMbPwE25noJSyPtBVvMrQFTQrRp/1R0ABItfat/HecfQrWTQ8FW88OE706BveZ3MwQNBphWoBUSzRPYduOLFnWZA+Cfwuc/AZhmcW6OVs+u2ucPAbwnHD8PwBbt7xsALBaOETAN7G3a56kAbhWOewA0gG3xnwZgK9ik6Yny3R6n1fkXrf5DtbL/oB3vCxZH1QPgALDJ723h+hatzJFgWpdvAXzmsC6Pg2nUe2n3WarVpZ/pvMFgaXIPsChnPJh27sQI2pqt5hfAmWAC5UjhuwUAXgWQAaZxrgCQK7k2as0vgH8D+FL4nKW987DtHMF+kyIct2tXVwNYbVGPRyBoLMGErGYAnYTvrgYwW/t7FoA7hWNnmesiHDsWTLCSHbsJwHbhc6ZWTl+Lev4AFl8YYGNEo+n5S8D6jVd7T6NM7XGB9veVAOabyn4bLAU3wBbodwDo4rK9Gd6j9t1WAFUAjtTa1CsAFgrHjwdLv50JtlguAlvQmMueCOBDF3U5CEz4PwVMuPwX2K7bQ5Jzf4NFP9He5QlgJkupLt+HreYXbFGzXqwT2Fj5D+3384HtLhzppn9CaX7VP4f/lOZXkQzkuTh3CJgdWBUhpApMSPCDTdpmBoFtF1pRJPzdACbkgRDiJYQ8RQjZQQipQVBb3DPctWCaNf15KKUUbItWrP/LQv0rwAb9AZTSWQBeA9NcFRPmYNXFpv52NGr/P0MpraKU7gab5M/T6lVEKd1EKQ1QSneBmaFcbrr+A0rpVkppHZiG8DyH934CwGowrc0iMMGlFUxAEbkBTCjZZS6AsIgJU8EEnvkO7+sIQsgxYJq/yymlW4VD14ItBPLAtLKfwfjbxRJzO6mHpuXXcNPO+fnSdoXw/cBcTiqAQqGst8G0pCH1BtPqWjEIwB5Kqc/iuN6HKKUN2p+8D55LCFmiOTdVgbU9sf+Vm8rlfbAX2AJBrKP49xAAR/Nn08q+FmyRBjBN6HkA9hBC5hJCjrV5vnA0AvieUrqcUtoEtlN0HN8BoZQupJQ2UkobKKX/AxOUTxQLIIR0AvB7MG28IyilW8C03a+BaW17gplyGNoyIWQQ2M7Cxxbl+CmlC8B2JP7o9P7h0J5pMoAl2nNzbgPT9o4FE9qvA/AzIaS/drwt+6diP0cJv4q2xGprW/y+HkwTAkDfOu0lHM8Dsw3MEf5lUEr3ScrNAzA8gnpeA2YrfAbY1upQXh0H1xZC2L7WtnHF7ew8sG1Vsf6dKKWLAIBS+gql9AiwCWAk5I4jTsgF0yRavXMzFMbnW+fiWmNBbEK/m1I6gFI6DEyoW0kp9ZtOvQGSSZ0QMgRsm/4xSmlMQ7cRQg4D8BOAWyilv5nqvYdSegGltBel9GgwU4Rlsby/QCGYcMjrlandj2PXzmW/i127susH5rLywDS/PYVyulBKuVmAod5g2nsr8gAMduvISghJB9tpeA5AH0ppDti2u5P+VwqmNRT7nFjfPABzTe8pm1L6RwDQBNWLwYT9H8BsrSPF3If431bPYe6DADNjqQDbqXIMpfQbSuk4SmkPsJTUQ8Dsn0VuALCIUrozTHEpiGwcDUH7bX8As6e+w3T4EACTtQV3gDKznEJoZmNt3D8V+zlK+FW0JcUAhoU5ZyuADMLCUqWCbbmlC8ffAvCEJiCBENKLEHKxRVkTAdxMCDmdEOLRQuUc5KCencEEgHIwQfxJB9dwfgFwMCHkEm3SvwtBrRKv/0PcxpAQ0pUQ8nvt7yM1e7dUsEVAE5i2zzWaJm0SgAcIIZ0JIQPB7DV/1u51CiFkMGEMAvOy/lEo4gOwdzdME8we5Ndq1+8mpnBfwrEBhJD+WtnHgG27/sd0znFgWsmvzdeCba2/Til9S1L2TcQUQsl0PJUQkgE2tqUQQjK0BRQIIePA7F7voZROllw7WntXaYSQ68C29F8QjqdpZRMAqVrZlmMoYWHE5lgc/gbABYSQEwghaQD+C+N4bNfOS8G2sYeZzpe2K7DfrS8h5D7CwtB1JsEQU8UAhvLnoJQWgjl/Pk8I6aL1m+GEkJO1878C8GdCyEDCbGz/bvX8YIJJIYCnCCFZ2vs63uZ8ThpYny8F4COEnAv2W4RFW2B9B+ARQkim1t9vEE75GcBIQsj1WltJ1frdaO33vZYQ0pVS2grmjBVR/9P4AMClhJBDtT79L7Cdjiqt7x3P2xQh5G9gGtqFpjKkNvFh2hYIIUcQtoPVC0xzP1nTCIvcAGYmIF7XmxByFSEkW7v+bDCzF9H2mRJCTrG4r0frI6nsI8nQ2je0d/ANmEb8BkppwHT5cgDna2MOIYScCaYA2KBdb9s/FQo3KOFX0Zb8D8A/CdtuvF92AqW0Gsz+9j0w7UA9jFtbL4Np7mYQFptyCZjjjKysZdCcZcCiJ8wF04CE42Ow7dx9YNuFSxxcw+9ZBrZN+QyY8DwGLPpBs3b8e7CwRV8SZlKxAcC52uVdALwLZovKowA8BwDapOzW8etuMC/+AgCLwbb639eOHa59Vw9mmrABzMmKP8f7YO9hqVaXZn5cm8x6wPq9DNfKrAfT7P6dUmoO8XQjgO8opbWm728DE+r+Q4RYpMLxQQgVEETeBZtcrwaLoNAI5lQGsOgAvcAcq3jZ4js9GyyyQCVYmKhzKKWlwvEZWnnHgXmaNyLofCbDsq6U0o1gC6PPwQTESjhs59rC5gkAC7W+dIxdu9Le8ZkALgQzNdgGgIeI4ouPckIID+F2A5gAukmr1zdgNscAe7/TwcK+rQITNKVoguiFYI5ze7Xnu9LqfOG6WrC29pV2/2u0d+GUu8F2bIrAosx8gWD/qwUTmq4C6xdFYO+NL7CvB7Bbe4d3gm29A9DjUxvMEsI8xywwG9ZfwEx+RmjPArAF9pva8+0Di7xxLqVUN33RFoKnQW6WEK4fvAxmRpGr/f8H8SBh5hwDYVp8gmmf/wj2W1WCjT/3UUp/1K4bCDamrLe470lg/WIK2K5AI4Lh3Y4Dc2I8C0CV0Af5O/0YzCFyDtjC4xWw3QwutIfrnwYIISeaxg6FQoeYFpQKhSKGaBq1fADXUkpnx/E+uWACyveU0hvjdR/tXicAuItSenU872Nx7xlgdsCb2/redhBC+CIrA8AYSulOQsgaMKe1ctuLFXGFEPI0mCNd3PoFIeSfYE5rrWD2+/VhLon2fmuQgLalaVzHUkofasv7hoMwc4piMI3zM5TSRxNcJUWSo4RfhSLGaFuFS8G0Hn8D0/ANo5Q22l6oUCiiRjN1SAPTTh4JpoW8jVL6QyLrpVAokgeVUUuhiD3Hgm1n863jS5Tgq1C0GZ3BTB36g5kbPA+jPbtCoejgKM2vQqFQKBQKhaLDoBzeFAqFQqFQKBQdhoSYPfTs2ZMOHTq0ze/b1Nrmt1Qokp6S4gKkBFhEp/69rXIoKOJFQUkxfB4vAKB3n/5hzlbEmpLiAgBASsCv2r9CEQ8y0hJy25UrV5ZRSnvJjiXE7GHChAl0xYoVbX7frYXRhGxUKPZPRvUProF9uW6S7SliQcqoYA6G3AKrZGiKeKHav0IRX7wjB4Y/KQ4QQlZSSifIjimzB4VCoVAoFApFh0EJvwqFQqFQKBSKDoMSfhUKhUKhUCgUHQYV51eh6OB8O20pBtaXJboaHZal3/6C/Kyeia5Gh+XbaUsBQPUBhcLE8PfX4uLhOXjh5CGJrkrMUcKvQtHBGTf+CAyvLUx0NTosR4wbj5zO/RJdjQ7LuPFHAIDqAwqFhB93VO2Xwq8ye1AoFAqFQqFQdBiU8KtQKBQKhUKh6DDETPglhHgJIasJIT/HqkyFQqFQKBQKhSKWxNLm914AmwF0iWGZCoUizqgg/4lFJblILKr9KxQdj5hofgkhAwGcD+C9WJSnUCgUCoVCoYiekoZWXDt1Byqb1OKaEyuzh5cAPAAgYHUCIeR2QsgKQsiK0tLSGN1WoVAoFAqFQmHFxA2lWFJYh6+3VTi+hlIaxxolnqiFX0LIBQBKKKUr7c6jlL5DKZ1AKZ3Qq1evaG+rUMSFHaU1yKusS3Q1FO2ALRWNKGtsTXQ1FAqFwhFu5Fn//i37xkTzezyAiwghuwF8CeA0QsinMShXoWhz/vj5Itz68YJEV0PRDjj/h6049estia6GQqFwwfaqJgx/fy3WlTUkuiptBiHur/Erza89lNKHKKUDKaVDAVwFYBal9Lqoa6ZQKBRJToPP0tJLoVAkIbPyagAAv+ysSmxF2hAu+7oxZbA6dVJu+X6x46Xi/CoUCoVCoVDspzhV/K4pqcdHm5hPlkzzm1fbjH8szMdds/bEsHaJIabpjSmlcwDMiWWZCoVCoVAoFLFgP9/NtyXco//u5+0AgBvH9EJAcnKLZghc3tj+o0Yoza+iwxKgFE9MXYONBZWJropCoVAoFHGBRGD0q2x+FYr9lNqmVszdWoT/TF6V6KooFAqFog2IxPkr2Smqb8XXW8vDnudGnJVpfvend6eEX0WHxethPdkXSG6npV1ltfhi+Y5EV0OhSEoopfhg0VbsLq9NdFUUioRw84yd+PuCfMskFpHIrAGT5vf9DaW4ftrOCEpKTmJq86tQxIqyuiakej3o2ikt7vdqlQQ0POvlabjssCG486TRcb+/mZqmFoACXbRn//OkxWj2BXDFEcN0gT2WzFu1F0PrimNersIZe+ctx+7sPomuRrultqkVXyzfiakb8vHV7ae5vn7eqr0AoPqAot3Coy+EM1WIJs7vE8sK3FYrqVGaX0VScs3EOfj9O7Pieg8+EPj8cs3vd6sT49F6+duzcLnw7M1aOK14Zdzp07c/+vfpi/59+salfIU9/fv0RZ++/dGnb/82u+fu8lpc+PoMFNc0ttk9402kNor83av2r+Dk1Tbj1dVF+12WMzdP45fZPexHKOFX0WHhA1uyd3Gu6zVvQ9lBKcWvm/ehqdUfn0op2jUzNu1Dsy+AuVsLE12VqEn2/qtILszD6Oy8Gjy3wtgPbp2xCy+tLkZBvbt4tuWNPszNr4m2ijGFUqrPHdHa/O5PKOFX0WFx0rffmZ+4DF6VDc0Agk4Gbgaj9fsq8eyM9Xhz3uY41EzR3umUyizeGtXiSNEB2FTeiOHvr0VuZXCng4+rt/26C2+uKzGcX9/KdtvcWpndPGMnbpmxC80Wu4nxRpaS+OkVhXh7fanrstwoW9ojSvhVJITXZm/C41PWuLrmkyXb8cRUd9fY4aRzf7Nqd8zu55YXZ24wfLarL6UUk1bsRHldEwCg2ceEmhIH29rFRQUoKC5CQXFRFLVVREpBcRGKiwpQXNR2NnWd0rwAgMbW9h+vkxOpNTx/96r977/8sqsKAPDbXmdaWZ821npdhjfYXsXG37aXG1k9ZaYKX2wJHwUCYBpwEZkgvT+hHN4UCeGndXtdX/PJUhaA++FzQ48FKEVlQzN6ZGU4L7ANOzelFFWNLeiWme74mmZT6lw74Xd3eR0mLtyKpbtK8cLvj0aKh61rfQ7UxScdPlj/25eb57h+itgw+KQj9b9zC9pGGM1I5cJv+9f8RitoqPa//8NN3AicheviQqTbBRU1/d/WyMb7NC8BuPWGTWe5e9Zuw2el+VUo2gGfL9uBq9+b48qBJ9CGQ9QPa/fgyndnI6+yLoKr2RAcsBFkebg2buOb4rXWBCgUutlDS/vX/OqCTZLGIPUHKBbsU2HYEgkfBcUmYifbcc1vpNrPRCWIkNU3zRMU8+xqZTbU2N81v0r4VewXrNhTBgAorW1yflEbdu6Ve9jWU0FVg+Nr+GTuxOaXmkZ3rvltTZDt2f5MRZMPw99fm+hqREWql7UP8+5Ce6QtF7FOmLqrCqtL6vXP764vwY3Td2JOXnI5QrVX/AGKFpfjmj48EmIr9FY1+9DoC4CHfo9U++n0spl7q/HBxlB73H11Lbjkp62osIjba4YL9TKhO83rbFVovjTaSBeUUny+pQz1Sbq7pIRfxX6BR5MQ3QxWsVSKLt5ZgmdnrLM8HonTmhk3z8bvpzS/sWdndXOiq6AQSLbd2btn78HlP2/XP++qYe2lpNFd5ACFnFtm7MToj9ZLj/kDFKuEhQfHaRs54rONOPe7XF3zG2nTcjpW3zFzNx5farT131bZhJO+2oz1ZY34eWeVq/vKxvt0QfjdXWM9donCLqXUVvPrZJdlQUEd/rVoH55YmpzxgZXwq9gv4MkfXIUDMw1t0ax0/zN5FX7dbN3JuXBuvodVjGEg1N7MybPxa/QYxkmevS7W/HXu3navlW0L9NBHySY5umB3eS32VtS1G9vEJLXKaHcsKJCbjrUGKJ5bWYjf/7wdy4uM5wQ1v8HvrAS4vLoWXUnBhcnJOytDHMLskAmOv+yswuU/bwt77aWTg+eEazP1rX6DFlxq9uANink/7qiyLEucKSiMWmQxSgbgbDHBNb5VzUrzmzSU1Dbip7XuHa7smLmlAPd9tSSmZUbCh4u34YLXZiS6Gm1Gi8+PFl9AGgu3qKYBl745E/mVoZoAwNiB65t9+HBx+IGJs6usFvssypUh0/zuKK3Bea/NwOKdJfKL+LXa/3Y2v/xZ9lU1YMnOEv09OHF425/4YUdl3O+xPwgx7URetOX2Txfitk8WJP2zBC2S9oeWk7zcO3sP3tFCepU0MHOB4e+vxQsrC/UfwekvwAW/TzeXY3VJPe6bsxe3/bor/IXafWQLsj/P2YPVJQ1hF5yNgilSuFBr4z/ZgIt+3IZyzTzCJym71TQHWM0J4teUGj9fIexkOIVfn6y2+B1S+H34x5V4bc4mPY5qLHhm+jpsKqyKWXmR8vmyHa7toZKdopoGnPXyNOmxC17/Fbd/ugAeieb3ty2FqG/x4dfN+6TXiudOXJiLL5Y7z1t+x2cLcfPH81Fe1+RICOYTnzjwbS2uBgAs2uEsraqdbSPXYje0+PDvyav0wdvfwTS/CmcEncRCZ6aGFp8eMq89EOzHbTPLvrW2GHfM3OXYpEisXl2rP2ExYPd3pu+p1v8WlJ14fW2JPnaWN/kcOaPxn3bixlKDCYtT7JqGWRi1Q9Y/zWyrCvZVWZs0ywPFDeHNb8ya3waTb4ATgZZfnqxCZrLWK65UNbSwP5JcYxAN7Xk708z6faHavIr6ZuytYFtbBdUNgs1v8Bzu7JXqDd/MIw35dPXEObj54/lhz+ODhfirhDPV4AOfrjW2mTPNJfy2hZlg+GLkslvb4sepX2/G2lLnDnv7K+aBv71su4vwpiQbJ+74bCGunjinTesTDeZHKKpp0MeGePDsyiLM3FuDzxzGTxU55JMN+H0EwpRCjj9AMXlnZUgf9BBi+I7/+e76Ujy/Mv7xnGMm/Lq8r2y4P2lgF8NnJ4uvAKWGZzBX2TZSRoAyp0G36vY2pkMKv1xUcLKqSgZmbMpHdWOLq2v2p+1uWUe79v05uO2TBfpnXfgVntss/K7NL8dZL0/DrjIWdsg4OMbmfckEoS+X78SC7cUhxz2SWLx29QhQiokLc/GRA/OMqRvyAQRX71M35KO+OXKHm9Ul9dhb24IXV7WPdLjROvq1BmiInRvHPGq0tMeYQDbtzE24wGTA3Gdu+GCeYWyIFyUONGhA6MJ0Y7n1+11eVIdZDhMxKICPNpXhvjl78eHGMsP3KYQYt/FjeE9KKX7eWYkmm0gpsnmA+519tbXC8b08BMirbcYJkzahoC68DCAb98zjlRPhmyLyRf3tM3dh3MfrBc1vcspZUQu/hJBBhJDZhJDNhJCNhJB7Y1GxeNKe5MJ9VfV47tcNeMJlNjQnIa7qmlvb1famiLmTeySaVbPwywXQNfnlCFCKeduCGoBYKe9kdrnvL9oq3Cd43CsR2OVCW1BDPGnFLny2bAcA9vtd8uavWJtfbjm6EzDb4hd/24Bnf5V7SFsxZVcVTv9mM/wBs2tgeBYV1GL4+2ttvYvjiRvtingN/32eWV6A877fij0O6t8et7Hb0xgYDitzoAClWLKzJOa7YJ1S2HgytIvzhDWAMwXYVVN24A8zHdiWWvD5lrIOtTtTWM8EwieWGZ2NPcQovMVqd2ZfXQvm5Nfi3jl78cwKa0WArH9xBY05uoMdhACTcitQWN+K77czDfd32yosxzeZza/52R9dvE93Cl5SWIeN5ZL2Qu3j/Mr0hntqmvHokn2Ym1/LiwDgPkV0WxELza8PwF8ppaMBHAPgLkLImBiUGzf4YBgP04BYl8kFuAqX9slOhN+bP5rX5tubAUoxfWO+y6vCv1OuxReFx1afUfjlZgb+AMWUDXn4YFFQgxqrWKHhBllxzBLro9fZxrbBLBjnFlWjocWPz5fttLyvuIJftKMEGwucO4Q9OD8Pu2ta0GRqS3PzazBVSxdqBXc8W1EU3h46QGnMQ7K5FX6bfQEc9OE6fUt0dQmbEModxNlsjkLzKwvL1BboY2BC7h5brLrcT2v34t+TV2HOVqOQ4g9QTNuYj5YIYxznpLPseE6XhG1pFfOvRftw2WTnjrvtHZ/Fu/V6jPF8Y7U7c9JXm3XHt7JGNjZQYfzid1lQUBtiW+tUCBRPm7a7WhdoUzwE32+vxN/m5+H9DaGxgQG50G1uf0uFMfnaqTtw0Y+h7SUApnG2Qtam75m9Bx9vCmrg+bzjSdId9qiFX0ppIaV0lfZ3LYDNAAZEW2484Q0kHsHRY61RCTpKubvOifBbnYC4kzM378PzMze4usbNs4tCoFnz69XMDPwBGrT71m/iqkqWhHOmMGh+ufArfGe3bfXj2j2G73kYsxQPsRW607xe/e+/fL0UNU3GZ1++swE1G3aHpHblg6556Lplxi7cPXsPnOBEQDjv+60Y94k7rXQ4wk12iwvr8NbaoKNhrWbzPYlvSdrEZTa/j2g0v19vrcCQB3/GizN2tVlqYyD5hN4WXyDiXSirtl9Sy8wLSuuMk/iMTfl4YeYG/LSOteHcAh9yC3zw5ebh+ZWFjkPluR3r21IEsBNc9ifE8VLM5eAhxnE1mgWqFWlegn11Lbhp+k6c8vVmw7GHF+bjsp+MQmUkQuDc/Fqs0zT5XhJcjJc3+aRzhWhCV9bYitzKxpDMbU7YUdWEhxY4V1J9saU8xJyHv/MklX1ja/NLCBkK4DAASyXHbieErCCErCgtla9a2oqg5jf6sv74+UL8JAglsdb8hms4hdUNBk1qiiZQicLvnvI6PbJAONbklWNzHKNW1DQZBW4n72vuNqODwt+/X255rlhei58JNLrwK2iHvaYXG6tfLVxwBXG84oOXzE4ZCJ0sp2wwDkZ8oFu+pwxr8pzbkS3aYQytdvnbs3Dmd7mhdRW0GW7HLzfnb6tqilozE6AUZcJiriXMD3Hd1B14VnB84fdP0/qPPjA6aJ/RTKxeyY5FW5BsDrGvzNqIqyfOiUgbyx/F3OZ4/zA/605u8y9552+stQ87aLiv8yq2Oad8vQVF9ft/Ug1R2PMKqlUvIQahL9x4ALjvE99vr8RJX23GgoI6FNS3huw2FZk0vw4TrYW0K66p9XqI3ma9RO7XIwr8p3+zBed9v9V6V9DmeQvDtJ1dNc24Z/ZuPZbvPxeFCsoNreydJ6nsGzvhlxCSDeBbAPdRSkMs9iml71BKJ1BKJ/Tq1StWt40IXfMraTy/bSnAVe/OxvLdpZi6IS/kuJkdpbV4bU5w1RevAdGq3HsnLcHzMzfoDZxrN8VJ5A+fLsDdXy52dJ8HvluOe+MYr9gc6/LLFcbwYhMX5GLlnuDWye7yWj11MWfV3lAva96RxY7OIx3wBYFoZuA1RYCYt82ZB/DP6/fiGZtMboXVDdhXFdxWKjNpszYWMnOA8vomPK7ZcXMNbm1TK5okUSdkC6Bf1ucZFjSfLLX2IDcLVnXNrZhvel7ZRMllutqWZEsgG8pLq4pw9Beb9M+tDgVSX4CivNGnC7CpHh5hQ7OzdlCGneOLmWZ/ABM3lOrhhzTz0TYXfvXbxfC2y3aXuksvLrC+gC3eCqrdm4FYTe6NLT7pcY/LBcem8kYsLQyNHsEvf2RxPl5dEz5cYaw0YOvKGnRbVzsm76zE8PfXYpONg117RxT2Uogo/MK12UO0XaGx1X4cEDW/VoKn3e+aQohu5uElBK2SMqqa/Lq5RZ1WH6tmbvdKUhzYaEzZVW0wczDz8mo2x+zPNr8ghKSCCb6fUUq/i0WZ8UQXlCTN/enp61DR0IyHf1yJF3/bGHHZMcei3CotCgQX5FO9oZpfGaJAOXFBbps5vpkngA8WbcNvWwrwxXLmxDVp5S489MMK/XhFvbPtu2BGM0H4DRg7v2hmYNb8hiNAKWqaWvDKrE2YaZPJ7U9fLMLNHwVDn11jsqnmURiKqoMTEp+E7/tqCW78cJ6j+rw8a6PjuMTmyf+d+bl4bMoarMkrDxGCRfhVx0/ahGW6nVhyjmTfbTfaMju1+X10yT4c9cVGfTsxTes/ugOlg2LcRFZ5bU0xnlxWgMla2lI+ybR1Jr5YjFPzthXpkVMA4J8/rsRdXyyKqKy+XTIBAIXV7gU1q0fRhVvTcT4OVDc5i6Bz4Y9bcc3UHcH76fdlf32yuRwvrbLrR7GdEy79aRtO/mpz2PN43NulRfEL+yYSoNRxBIxYUN3sg7juFAU2AuO4t74svBNgtOtPcyxcMzUtQcXGa6YdBt6W7DSuXsG8zesh0nHnwQV5OO7LTQZfAqvF4es2C7Yvc52F8bN7ZVz43m9tfglTkUwEsJlS+kL0VYo/wcHL+H2IHWgEOOlAdc2tKKpx5pEriw8rwvt7XmVwawQA1u2z3wb/hyBgTlq5y3UkgFjy9PR1BuczkbpmZ3aQ/P2IcW3578sHluAWc8CwReaEDxZuxeVvz3J1jR1i2/NTisZWn/4bcoKh+KIbPKw0XA98txyPTVmD5qLtaC7ajpUbmEa7rtUfogle3YaOWc3+AP46dy/ya0P7Y1F9K0olE2yNKYXmWd/lOhrAf9SE5kpN+NU1v9px2XRmDpHoxuphfRkT7rJTmR12ioeguWg78rauxz2vfYazXp4Wtu/GgliIY49PWYM7Plto+K7KZUhGjr4IiMB+2mpy9+tKDiNcaPhu9R787dtl2LBuJTasW6m3f7syRZy+w6BZRuyEAFmbMy9o+HqqrUSPN9aW4NgvN2Gfg5BcseDwzzYahDxRuKQwviOZUtb8Xr7bHl2/W1hQa3lsg0n4Ni+W9N1oW21s8Jk8sF90i7Gkqy3SC78iCL/mdNAzHYbba8+xcmNR9+MBXA/gNELIGu3feTEoN27ItsgBhDgCRVS2gyHxvq+W4IYPmIbP5w9g8rq9lgJKuDGYT8R8EspOTwUAzNgkz2pmhc8fQIvPPtHDvqp6V9uz7y3IxaZCo0bO7SKwvN6ZRpr/pi/+tgETFzL7VV5TPu5xgXdPeR2+WrnLVT1mb41dfNupG/KNWol9lbj4jZkxK99MuIm86KP7UPTRfTj6d+cDAK76ZTuOn7TJcE6kq/dd1Uxz3+gLYNruKkfXLCqoww87KvEvkx1Za4Di+EmbDFo4Tr1E6/LO+vD2my1ae+YpRbdUNuHp5QVBswcHQpCfUuysbsLoj9bh+ZWFthmU6jSzFq5hTiEERR/dh7f+7zLMePJGAMD93ywLe89ooUGJLLblRYhuihBBOVaXWEX1EQXstfkV+N05R+N35xytt3/AmTbf6VDIT7PrQo8t2Yf3HLRXN/XhO5ttpXibm88EpnD2oiKUUry2plgfJ9yyV7JABti7EPtuhYOoLW4cvGQ8MD/PcsdpTr61YAwI9sE27f+hBfn4fAvbsfV6iGM/iV8dCLJXTQkdU53QXnIlyIhFtIcFlFJCKR1PKT1U+zclFpWLF8FVlvOB9se1e/CpjV0lx0mReyvYarXZ58c3q3bj1dmbMM0i/FdQewlsLqrCFe/MMgjpZgXmyN5dAQDj+ncDAF0QdIKdlrWwugE3fzTfkGDBHwhg6oZ8qUBMKcVXK3fhvq+Mvo9utR9vzt3i6DyxBpNWGAVbbhLCUyAv2VUaYotrW3aMQ3G9+NsGR21P1/tGOb64rfvmitB3kxLhSPH2+lLsq2vBf5fsw12z9mCNCw2yeSHJPdh3RjhRyuCTlRjO7Z31pYLDm6ReZmEqQPHN1gq0+CneWFuC22ZYm6N4hGsAZ7Z1Vuwpr8Oe8si2tGNt8xtt9+BtPJzQ2djiw+5yoyBhJeQFhLFTJFaCrUzRIduV4Nj90h9uKsP/lke2wJ6dV4Ph768NcbBy+pv8vLMS6xyYBYQjkiZQ3eLHi6uKcMa3W5BbITd5mb67yvXOE0Xbhpizo1Z7RjO//zk4l946Yycm76zEUzaxgwGgooktnq0c3toap058yUh71lpHjFW0B5mQwc99fc5mfLxku/RYuO+sKKlt1AVZq+xbopD0+bIdqGpswcaCKvgDFKvzykOEST4gc4FHFAR3ldViroUGkxBisBM+6+Vp+HhJsHNy21txS/b7NXvw4m8bMHVjqGOguV/e+OFcfLPKXtt644dzbY/bYX7vi4Xg9tFqpQKUuh5oluwswbsLrBcebTkw+2n09qRuNL8VTT6sEQLtlzf69K3QWhunkHma5sjK3nZfHesjkYy3Lf6ArfanyRQwdJHm4CSrrfmn81PjeTz+pwyvbuNLDZ8j4Q+fLsAfPo0sk1k0dqi/bSnAha//avgu2iQCuqY9jO3zIz+vxu2fLjQs6Kz6d8CB5teKndXN2FppsUC22KKubPLhmC83hZ4uqd6yojp9F8ANz68MHb8/2sSiJ20oMwqPvI2FUzjcO2cvLv1pG6buqtLNf9ywqbzRoLlt8gXw1PICR46gYs3O+2Gr4djWyiasKKrDn2btweUuU0IHKA3rrNoWQ/Dyojoc+qk8tOeqkuAYWdrow31z9urxxcPhIUSa0KKtcTIvJEE1pXRM4Vf738mAbec4Jg0orf0/fWM+Hvl5lfS6NC3SQIOFLQ4APPjdcrww06ghFKMVvLcgFw9+t1z3Gtfvr50uc6C547OFeGKqdQxL87N+ujR0K0ScSHjK5domSaQA08sprG7EO/NzbT0/I3F20etl+vyfyauwXHPq41WJVHs7Y9M+1+mln5+5AV/bmFa4ERaiXVzHwpfKjYx2zZTt2F4VnAwDlFo+g/gentSyNFEL4YJ7QvfJTHVeGY175+zFkZ9bO7A+ukRuJiQTrMz18gWoYy0bfw9c45ySIM2JVfNbLYmkArCF+s/r9wIAnpm+Ds0mE6lohV+nERj44tuYmlx+rt6OTN87cS688MetOPd757tmgPN0xwBw9ZQduCaCrWZZKDa+/W0KYOP6N7l79h7H8btFLvxxK874dovett9dX4J315fik82hkQAafQF8t60iGObRpornfp+LKyPcjqcI35b21LTgq63OHLsiwUOCNv7hqLKRBWQEKHUc0SaebKlsxEU/brU9J1ljBaUkugKJxDwG+iSNqdkXQFqKN+T7T5duD9EEA8EBhydyeH3OJtx1SjDhXWF1gy6w2g1Oq/NYp7xw/CD9O69ghzh3W6gGoNUf0Mu0s52TaZkJnCXGoBYfKuqbkV9Vj/EDumv3D5b17ardpjvFDoLwW1xcyxXpNlEkUT/CCctOahKrlJxuNb+pHhJRemDOtiqjWYLd3cX78O7Hk0aYa8AdNzqnhfbHcMzQPN8ppa7s1Bw5FlFqqGxJow9VzT7kpIcOr7wPx8LsIRp0rSg086WN+Th37EA8KMTQZo6hTKJ64NvlKKhuwKkj+0vbrixspBPW7avAvsp6x2YPHgL4YRRs+LP4/NTQ71bsYRrRn9ftRc/sdFxw8GBH93CKuZgmC2HE6m4byxtR1+rXnR8jhdutmyPY8PoRsK338iZf2JTMBWGc1SileHTJPhzUvRNKG32459A+Iedw+3mZ8PmXOXvw694aDM/JwCG9MiOy8XaCqUtKmbGnWh8X4oEHwVTYscYXoHFJ3OGWb7dVhj0nGcwzZHRIzS/HvPUnEzasnMC+W73bqlADP67da/gshrLyB8JPxLxKLX4/5m8v1q4LoLEltF5NrX79fL9Nx/jbd6FJIsxmD6HHres4ZX0ernpvNu7/Zhn8PLyY0ODfnu/MbjcSjhjSE0BwsSDjpd82or65Va9bMuDEFIO3x2htft0+dqqNQBZJVezGPp9EiGnUTBAWm2Krcm/udImhmUyDuqemJaRPt7gciGVjgvmbpYX1IdqNEyexUFTNvoDB+51r58SUpZFQWO1se9QfoPjbt8ssNboAi3rwyqxNehg+zrMzghFgCrT7WS3IIl2o3f/NMrz420bHml++hW80e2D/1za34vfvBCOyNGtCWE1TK16ZFTRHcBNRwq6fmo81htnmf2NtaGipJl9Aeg9/gOKXnVW27/V/2k4J1/yaTxUFy6unbMfp3xjH4am7qkKiuliZ/r27vgTFDa0oa/Thk83leHhhPl5aVWTIhqfvqOplhRbG/Qn4XBMv+S1AacyUB5Hio/ELM9caoKhyGAkp0SSDhlpGhxZ+zZ3DSvMrctbL0/Dk1DU2ZUZ+f44YIYEPYGVCik5fgKK+JbThN7b69EnYFwhYTiTbS+Ten7bCr0zs0b4qFgLbl2h/W9071s6h540b6Oi8S9/6rc0TCViR5vU4soOKVXXdaldirYy0EyCMwi/7f/4+uWf0tN2a9lZyzMp2tsa0SHSbSW5ndXOIrbD5d5m4sTTkOx7z857Ze3DSV5v1d8D7UTD7mKvq6IiLaDthrrqxBWvzK/C/aUZzJ7G+3PmzybTQn5UburtkZTIQrQavsqFZWv6KPaUGsyqiR4UInudWyHGjiTKfOnVXle5YZn4TVsIvV7KYd0QA1h5kbfLwzzbgz3P24Ntt1uG33tvANNv8enMWM94sCDE6sb66phhf5pbj7tl78LufjSEmPZJxfmtVE55aXoi/zNnjyEad/xwyZyjdJ0U7Z5uVXXWUvLWuJGbjZzTweN6xhgm/7m3GE4FbhUNb0aGF3/K6ZoPtmmx7WDbYz9laZBkZwY1zlThhiFeJERJk5f22RZ5kwaD5pdSVpnNXWW0Y+2bJZC151JJaZuNkJWhWOkxa4RS+LeuESLdmY02P7HRHE3ZTqw/fr9kdlcNARX0zpjjIVChit0CJpCriaze3Z6PZA0VFkw8/7KiUns+jPMjallXSEnOTdmvO8dTyQpz2jTGhgKyXWP2ev+WxhSaf7Lnml9fjiWXWCVOsML/DBslCmGMVQkx0BuVjYIbEvMuMVb8Wh5oZm/Lxf18v1ev2+JQ1YZPVrN9XqZVDsbGgEv+buhZ1za34xw8r8ajgO8FlL78knrdT3Gh+zQufhxYG+xKlxt+iVrIbFw5CgEZJfXiCACdhw7jQa94Gl2UAA1iM2YcXasl2TOWb7YZrW/z47xLWRht9AUfjlpiIwYzuk6L9ceN0Z4l6+P2dLlyWFtWHOADuT/gC1DJ+r4wDc9Ix9/ej41gja5LBPENGhxZ+/z15FV6cGfTElHUsJ3awIlY/syyBhhNNpEx+LbJwDGts8Qdtfv3uDOKrGltsn9Wp1pSnVbbSBH26zOjAMCAn02EN5bjxlp/kMrZvPDh7zAC0+p25AGwuqsabc7egMQKvcJGlu0pdne82+50Z89UBWCdrEZtogAbzwZuPi5OurG1ZvU+zNsyt5hdg6Z13VTfj1dVFoJRGFD2E19mja34ji0LS6g8YtvABSFNi1za14k9fLMLeSrbtar6X+JHvbqU7sD21FH6FAp/7dQM2FDBh9rctBZi3rQifLt2Oxhaf5a4TxxegePjHFZi9tVAfM3eUBncCZMJ8PDW/Z31ndHqrF9onpdSwmGq2Gj9tbkep3ImN46S98nHefH/+vZvevL2qGcPfX4vDPt2A+lY/nl9ZiCWa+VGqh8DOsoNHKhBtjc3wyyPRQzy/shCjPrROLW/m/vl7w5/UTmkNUFS7WGyleT3ITkuMuGd2yk8WOpzwu6PUOPjyQRqQD+yuhV/JQLx8dymueHeWIaUw4GzQlolJVoJlcW2jPtAu3V3qKC6xyOKd1oOw0/SrD/+4ktXR4ejm9Ly7BadBkWRNnWhFWooXLQ41KInCbj3h5G2btzuZxl1+pTEddaid3pnfbsGUXVWG82TygFWfMGsdIh2Ir5qyHS+tLsa1U3dInTzCNWPezkXHLlliDsD4plp8ATwxZQ3ytex/C3cU4xeTJl+2OFq8sxjbS2rwmRaxxfxexXGKa35FG1+OOf211Tgga88+fyCYKY9S3PXFIvzpi0X6AlnGh4u3oUGb1Pl54m8vsw12GrZt6oZ8PDl1jasdMbPZjPiYAVPdzO3SF6D458J85Ns4kVU2+zBxg/Xi1E04K7OgHI2jUU2LH7/trcEnm4O24qlejyvN72NLJbsaXPNrUbfihla8tqZYas/6wcbQ6BF2RLLQbS/UtQZcjWUEQE56Cg7rHZ2yKRKU2UOS8MfPjbnnS2qb8M2qXcivrMcyiYZsY0GVq/Jlv/OafGa3ZRa8AwHrEFDiOWaKa+Sa3/zKesPg9NM6dyvfn9fLt8cbW32o1LQwlFKc9fK0sIJ1LIXfEb274JRRfaXHIkmHmigOH9wDaV4Pi8oR5rlT2zB6uDe7O7zZ3dGvV28A8gWFm/HLY5Keb5i+U7fjLaxvxalfb8a+OuaM9sWW4OTaGqAhAsTe2hb8Zc4ew/1lgq7V+5yjJQAQ7xEJPHbv0qJ6g1mGfv9wwi9lCRC4nZ64bcnfvzebRUohhCXVaWjxYWNhJeZuK8LLs1jEEdlv09hqFBRKa5vw2hxmqpGueZub+5nYa+yE0cemrDE+h6XZQ+j3zb6ALslP2ZCP/CqmGXQqrHJzHS5wByhFnRapxhjtwVFxePG3DZiztUgfyzje7O7I6dEHqdr7t6LRZ1RFUAqDJtQsGG6paMQXueVYZ7P9Xm8T9xoIam9LGlot7WN5mzAv9HymBZdbzJpkpvl1oIm2eST+jqyE6Pvm7MGLq4pw6U/ydPcKRnWz39Xihu/m3X5w73hVyZJkXYR06FBnnHfm5+LzZTukdrxvz9+CM0f3d1zWmvxyHDW0l+G7Rs0mr1Oq8XU7cRJxoyBsaPEZBudUL0GUO+YAgD99vgj7tImLj2sfL9mOK484wPIap+G1nGpAvUS+TjM76cQaHkrNiu6Z6ahoCG/HfP64Qbj39LH4YNFWtPjDa35TvV60+tvGm3fgXR8DAHbccggA+Yp4bpj0nCJ2K+pvt1Vgb20LJuWWY1jXDLy5LrjbUNXsx92zdodc46NGDZhsvdM5zYtKiQ2cWVv03oZS/O+EQSHnRYtsIhKdlabvqcaD8/MM5/OEAvz9cwKUJdVZtbdcD3XIBZxUs1EmQs0enpmxTv+Onx+gLAyYzx/Ayr1lUs2vFWKoQplTMCAfy1p8fqmjrFPbe75454Lu5HV79b4oji9OzUc8hL3bIpPyYOBdH+PUUf2wbk8pym2SPFw/zWiyFYDRrMbcLrMdhOSrC7N1zTOvnfz1ZqkQ4RMUKCFmD5L37MbUxixMp3uJo+gMYl/4aUclzhjcBf83dy+uG91T//2s1lt8MWCVtljBKG/0YVZe+LTFHK6PSERGNmX2kOTYpfZ18+M9O2M9rnpvNkb16QoAGNWnq+6QkmkaDMVJwGpMcuNF3WoSqmIV3YALvoB1pAgzMTV7oNa2vTJ7x1jx1KUTcIWNgA8A3bPs42ZyuM4o1euBP0DxkSlG9NVHDjN8TkT816+3loNSGrUpiRObYQpIBY0tFtqtHYKnPO8TvkAw896gzmnS68z956ut1t7z0SDz9H9AEHZFwRfQbPbCOKws3lmCKeuZYxJvD7IdgcZWP+qaW3UTBdEBjsco9weA378zC1dPnIPnft2AvIpguthwuydiqMK/frNUeo5sMdfs90u1jpGa/GwSduEi0fza+Qd4CQmrjw7JvkWpQYgLdSoMX6faMOMXD5NnpT1r8gf0dxxi4qO/mOBzu3nz5vLSPMTRbycKv3+Zuxd/X5CPX/fW4LZfd4VNMLWxvH06qV17UI+4ld09I3QRtdZlOmqiC79tP68oh7ckp2sn66xR4TQjZvwBqg/O2ekpuk2eWTvkZCBZttu5s1KLLyB1ZEkEToVfJ++Agkonrn+ff5i+yIgHhw/uiRSJpg0Azh07EBeNH4zOGe6yjfHsftyGk2MelGokWfPizd8X5GNOfm3Uoc6cjq9utu0umxzcBuVt5rgvN+GYLzZq38mvk7WvSFK4hqPBZV/zUYpKB3E6F+xgsWF5O5RpUptaffjf1LV4bMoaFNU0GPpeWkpQ82u4RhvTqOSYHbIQi7LyATYeyZpCpGHRMtOCO2e3f7pQ/9u55te6YXoIce2AGKDyUH1uCLcACmcW0eQThd/wbdCNHGIub8ruanyRGz4jmjnKxPaq4IKWv6N4JbdIFP84yvnusBX/PkZeRiz8WngZdgvAyw/sJv3+ieOdhRO1Qtn8tmOsQovZwW3TfAGqaydfmGnM8S0TEDcKDngA8MMa5+kmzZrfeGMVPaHFZx1j2IzT82SddnivzjigZ2dMvecsR2VEgpXwe1Dfrrj71DG4cPxgV+WlWmT8MdvJJoqG1kBUg+3knZV6mCYZ4Wx3ncDlzPImn27qYFWSTCa9wbR9HQucCL9dhZ2fDWWNrkIVcc2v7J21+qmeyru4ptEo/Fq0XzHUWbQZmP70+SLM3hIaE7iqoQWfSFKki/5mPbMzHN/HSgnhVDlhl1CIEPdh/ChCQ/WJOBmLzQ51Zupb7cf0Zj/VI4hYaYdFof4gF9ESZO3CidOZ+Tr+jK0BqoeO81N36aCTGQL7xEBOuXFML+n35h58SE/3TmtWZg87bjkEA7JTccvYnvj9gXKb92ifLVltfjuU8Gu3sq9utO6In0oG8HDwycrnD9h6SJub1S8WTmdOmLF5H8pjHEc3Ep6avtaxYGOlSRKhVL765QKxm1i/bgnX8U8YEZreU4r2OtK8oVtYL195TMyiVjx+8RGur2nYvlT/5/U4T3KxVTNRyK1sRLM/gNoWP+6bY+9kKbaL3Ar3Ae5TJVuvH24stdwulQkOmyK4bzjcRoVZXFinxwAW378VKR654xrANL+cv327HDVNQXtJ69Bk7P8dpbXY5tCUyYrtpTUhIQwB4P5vl+kJNERW5wUFKCfvjbdHK/v+/01zJtBZ9bGG7Uuxd808VOcucVQOJ2AKdWZ+1U7WFFaxgYd3TcehvTJBYa/9rW726+9nS4W8D0Qqe0S6KDKH2KyRLPL8AYpPNruL3pCsEOIu5KYdT50QqmU1K0bSXBjuZqbwHSOGzOxh3hVj8PDRAywXh9ELv8rmN+G0pfadN9iNhVV6APfQ+gg2v9wm1GUucHOzNG+nR0tNo3vHgwXbi/HGnM3hT4yACVo6YyB2Aw4A3H/mwdLvZQ5G0ZAm+X0H5mSFTMzP/e4oV+V+fftp+OK2U0KcLTnZ6da+raXfPqb/CyeEi6/83O9zUdrQivO+34r/LNrnqJ68D+6uacbPu6ocXSOS5gl1uuEhlfplhZqgtJXSYaXZHlSCOS7nPM2JUHz/VvAYvDL7XLPduxjNwEq45OG+qiLo39Hyv2nrcM3EOQCcCb/c3KE5Svt+K3vH0m8fw7QX78XuSY+6Ko/CHOrM2NictG8rze+InAxcMZJp4uySZ1z441bs0JK/LC2Sj/2R7rBsjTD7mlloloX0Y0mYklMj6BY+okeikTXz+5GhtsNmWddK+M1M8eCt04cavuudyfqOE7MHq0NuhG0AGJCdihE5QV8YpflNCtruR3DSsWXnpDvIsiRCARzQI9vVNW7ILa5u0+vs+PK2U/HIBYfpn60iQESCVWgxK7MHt/BfWiZMewgJGZTGD+yOu08JZuS57LAhlmUfNbQXunZKQ48s6y3kPl06GT4fc4BcSPYS+8nSPI7xbcyvt1VYap4M12ttfsquyNpHmpdY9i23g3SicTMazdtWiG0l1fKoCjYCpJX2LtFyB9cIt/rDC7S8b0SrQYr1BhGlZrMH4/HX1hSHLaO2xfqZslJZhde5dG4yU94YmY37r3sj2xGwil8t4qcsikus6J4RfeCqIV3kTrPh4BrTa0czwXVsj052p7vGnHLaapzLTvVgRI5xDuiXxZ7JSbQHq8WhWyc5D4hBidISiCwxULyJyXBACDmHEJJLCNlOCPl7LMqMB2054DsSfiUNIj0CYcvOlm1/gL+n7lnpuvc6YFzF9usa3YCTYjEzWn0fKTIbTA+Rb8ledEhQ4O1uIdimeT2OTB06pwe1os9dfhQGdsuSnre3tsXWO9ccPkn8fNWU8OZB0SoB0rzEUjhPk6gu9pee0dDix11fLJZqfu20p1b+Ck6T1sQTSp1loeTPF21kl1gnxDHb/M7Ldy8s/pYnXwQGKEUnbZforlnO/T5kiOEEk4VADGzNY41ZyHQKb1a/O7A7dtxyCH66eKT0vKVXyxM1OS2fk2YxJxESKtzy3TDd7MFG82vVPdwKv4SECpbJaPoQ9cxOCPECeB3AuQDGALiaEBLZrxxn2nL14aRjy+Yft2YPQHyznCXD8OS36DhiR554/YlR3cNKwxvJ7yGDtz2Z2QMhJKxWqpeFY5DTCURMXTt+gHUw/8eXFqCs0YdhXdNxnSR8T6PJ/vBil8HonSY4sCLN40GAyvuyzDbNTQrQSBjW1Vmou1jxjCQLm9toNIB9You2wmmM7oYWP+ZvK3L1nNccNTzku1grCfyUokDI3rbKgemLGSvNb4BCF373R3yB6ENxnjyws/63k182U/I+PzjrANx1CEv8EOnrtru3aIbQQ9BOp7vYpfrzYUa/EisbXAISEiKzfzbT/DZpi8xINsfcdhuZDXQyjDdmYtG7jgKwnVK6k1LaAuBLABfHoNyYc99X1g4lMtKjGHycpND000BIz0mNQNMYT8VvPDOojejdxfBZDGVkqIPFICl2sBSvB/8+/zCM7htZ6DMrswc3xv6j+3bFPaeOwWtXHWt5jszhzUrzK2Il/Dq1e3ZrTnN8/2ycNST0XTZEmVRke1V0Dplc8yvTTidiB6RPprtQd/EgkonFqeY3ngvrqgbn9saPTVnjSvMr01bF+kneXV+Kv8x1l0XTKX5B87s/4qcUu2siHwuO75+N988aFv5EAVnILtEMLdK2LrtuxmWj8PUFI3CmMIaK45OTey26agx+u/wgXDrCqKywvJSExofn4xPXvNppcWWOlT9dfKDBFtjJdOORBGRMZNhVK2LRuwYAEEMU5GvfGSCE3E4IWUEIWVFa6jx2bSzZXupuW6pbZuRaHSerWsP8o53e0OrePiueE1Q8k0h0N73fr28/DXefErppYDVRm5/7hBF98PKV1oKnHZZmDy7MUF6+8lhcOH4wene2NsGwtPkN8xtaLQzsfvu3rjke/bsyJ4z0FA+6dkrDpYda2w6LeAmB7K2Hizsab9K0LFN5kgxQbRnmj5MM8smmwirX1ziNTmFOzBNL3Ai/QGhmNjtkcduTcevViv1e+A1QLCioi/j6w3rLzbbs6NkpJSRhRCoJmkOe0L+z5Kogx/eX+9bI9CbDczJwuE0dLzggx/LYMycOwlunD0WfzFQM7RIqg1glQCIwCre3ju2FzmmsDfGR0W6ekcWJ9hKj/a6TZB7M/KJjaH5lbzNkFqKUvkMpnUApndCrl9zZJp6IGdzOHhMim0sZZGEX6QRHwq9ksp60Qh4714zo5OYh7qMDOOWp6c7jQrpFnFjTUzxI9Xpw0SGhcXOtUqpaaT0/vflkvH3t8a7qYhXVIRJNvJ021trswV74TU2xcEawqd6wXp31OMTpKV58fftp+OPJow3njOvfTXotSwUb+t4TLvxqv8cra4pCjiXChDDajEmDsiNzshHZWeY89TTH6WRkTskeS+pbjOElowne0inVixOGB7eHc4SF9e0njgIA1CYgcUyk+ANAxn4o/A7XzISitf2PxGRC5hOQ4glm9svJ8Oop3mWcLdkJi5THbBJH/O7A7gaNschTJwxEf0lUG4D1H7HJ/OPo/sjSdvz4UJ5hMY8AwIkDOoeYYxBiFPACFHjjtCF494wDLMvxkFDn0v1V+M0HMEj4PBCA+6wQcaakJhi2xakTU58unfCX08dGdD8ntpjNPj/K6oJbP5NW7HRcfpdOwUmTEILxA63tOJMVcSveTmnnVPPL6d2lEw7oab+KD7nGQltrHerMehCxS00s1/wGn6Vzeqo+WRvLlNejf479Ao0LsGazB74FZxX1gYBIfxO3mcxiDfd0lkWLSIR9erTC7+unO9PEu+ViySJSxKkWNSPVveb3kIHdMdJk0iRjbb4x1fTBNrbo4Th+RB+cPLKv/rlbZnB8jOQ3un50/NLVOsFPKTJiHGYxHH0y47fQ4fD+G22GN/H60wcF29qk80dYXpPiISEb8imeYGa/cK3kxAGdsfWm8SHfWwmqnAVXjsbPJie4SGPn/n5kD0N7/ufRwaxwJ/TvHKJ46ZRq1Pz2zwr2ix8vOtBwrocQ3Dm+t/E70xvzU4qzh+bgtMHW/ZsgdCczGXddYtG7lgM4kBByACEkDcBVAH6KQbkxpUFwfElxaPWd4vVE/KM5sfn9eMl2gzf2xIVbHZc/ICcYU9Dt9mEsOW9cdKkPOXZDYaRewVceYVydeghw1uhQrf/jFx9hKeQ6bSsiMs0vH6stNb/aYHH0Ab1w+eGhq2pZlIh/nHsIHr/IPtIDdxJKdynEUAvXNHO0Bye8aYo96ZYHj+yn/22nDUuE2UO0oabjJeDESmMbSZzrVK8Hj188Iex55l0uDyEYP0C+ExEOLyEGm8ouGYLwKywcTx3ZD+HonOax3bJuC3wBik42WjorukRhplLcEPu032b4OPfK6vBh4Ozg0/KOWw7BO2ceoNvB2gmVqV4SYi+bkeLRx7lw5oMByMf2J8Ok/+2XlYbRNuHP+mSm4MAcexPLAwTzB75LePHwHNw8thduGtMTQ7uk4ZFjB+jabb54405+XMD3egiuO6gHnjphIMZJ4hKbh1APMdoY3zw2/K49IaHj4n6p+aWU+gDcDWA6gM0AvqKUboy23FgjCrFpJi2YlV2blxCcKRGWnBDPAN6vXXUsrjky6M3sJEtavDh+uMMMZxKcLiycLCRk3HrCKNxz6hi8cuUxmPSHU/H17afjumNCvcAppZZCjJXNr904aWv2IHF4E6+x0ojI6nHKyH7onmU/aOrCr0shhiJ2wqTMcc6K508K1ViKE1qGzWLEaZdz42kdDpmW/weTRsUOO7vOSLL1cSLR2MqIJJFMqteDnMw0nDG6f/iTBTwEGNMvMuHXLLhkpafg+qNHoF/XToZnyLJJ9sLp5PUgJz30/bVlGGk/Bbqmp2BwZ3dmMeN7xjbGbKzhP0Uki2gR8zhJTP9zttw0HgM10yJZX+2ekaKPG+E2CKySNVilEHfK1EtHYdplB9me88slI7HmunEAgu2Q62T/dcwA/Hb5aKR5PUjzerDmunH419EDDOeKr/vR4wZKk2kACPHzIMKu5NF9s0LiCMvwADioO2uHD0xgi81IItLEm5ioHSilUyilIymlwymlT8SizFjTKqw8zI31uzvPkF6T4iHITEvBKcJ2mlMorCMIWJ3vlJF9uhqEoXAT1Kg+ocLHwJzos9EA0U2yxtVg6Bvo05l1tAN7G+v/9rXH4y+nj3N0jwvHD8ZBfXPQLTMdnTNS0bdLZsikHKCw3PMyt5XB3bNw4fjBOG2U9cQu0yDoGfwsBsouGcyOS9yuFbEzpbCDv2Pzgi9YL2DAiLFI6zMcaX2CCwNK7YXJY/rFJ7HKEX1C22WK8D4jcQIyB53nn8+3cTpxiuy37usiAkS614Mxo0LfP2AdbcaJSUGnBAq/vK26XTvJkr2IcOdNq2vFxVpmWgquP2YEPrrpZEPfke288HfffSDbmu4kSRYARN4HI4ELd1YaaCtN4d2HRq6MaAti9QYvHiFfJJm7Y6onGBc8zRMayTcn3au303AjS2ucYmM7cVhPT/Ggs6ak4yYFVpd1TvPq/Yif60YWESEg+oLF6XrFQ4CHjuyHz88djkN7sT6bjNEe4m/kkyQYNL8mAcSq8XkjWNF5PcEMVKlej2UGo66d0lAtpBb9cLG7eKlinQNhWqUYE/XC8YPRNSMVlQ0tyK9icSlPPrAv5m4LdSA66cC+mCf5XsQs/N550kF4a94W6blpJjOSZp8fD597CJ6YutYwUU667VQEQNEjKwO5RdUY0M046R3Qs7Nrm16Rv515MBpb/CioqseeinqM7ptj2QbMtrZdMtJwz6n2YaztQm7JJl+ApW3+21kH4+QD5Qstscz3rj8BWRbRHwDm8MejhjTpZg/Wbfmvr/+Ml2cZN2uY5tfykphqT0Vk9pmiQGQ2ezimXzaWFDKvcVE2uWxEN3y3vRJAcMI9vn82rj2oBwrrW7GqpCHE83tUtwzkukzpKvs5O9m8azM56V5M/+InnDApNB241YLFCeZ+2T0rHRX17kNLRWIvyxfmTncOhvXsjJ1ltVixpwyj++VYnvfiFUfjyndnS48RYhwHReFfbD+ynZd+N70MgMWNnZtfi8wUjx4fVSTVQ2wTwNiR4WX2lGvLGjA7L7yDIp9DrNZ6VrU4sm9sF6X/PLo/Hl+aXC48fTNTMd60Zf/aqUPwzvpS9JJE+eDtMMUTKnx7CEFAe5tW4/Zt43qhwRfAQd2Mi2iWDdNd3Z8/aTCGmxYubodS3pydXDaqewZuHdtLzz4XDnP8dI/g8BauP//nmAF4dMk+nDKoC9K8HhzdLxu1LX5MOm84Rg+InbNgrOgwwq8Y2sdp4oJIVvopJuEXkAu/0W4DiDKZGxOLnlnpuPqo4Xht9ib9u+uPGYGjDuiF/Mp6/Lh2j24fnWEz+Z45uj8KqxuRabIttLM1fPbyo3DvpCX651Z/AEN7MCFWfIJuwlb+qAjj9tpBCMF/hDTJAFDfLDcdMdv8RhtVzkrzS8KY2Ij3HdzdfoLrLaQybm6V/5biY8jqRGGfkCKSTIR2sOgS4TWNZuH3wQn9cOlktnAUr3z6xEFB4Vc74CUEZw/NwUeb5KEW7xzf23XcVplw2MnFu/F6CPplpSErLSXEfCndwkTGCWbhd3jPzo6F3wE5mdinLYxlv8cHN5yIfjmZOOeV6dLr+TtxmlSILzyzM1LRaBNa0S70pNdj1PyKgoxo82u1+ASgC7a8jR3QJR27hFi00Tg3XjqiO+45rC/+MmePo/PDhaayG/LfPmMoZuypxrfbKl3V8fRBXeCnFHPyg8I519zFEnHB6pbHjhuIyyRa3wl9szGhbzbKGkMjenABNcUTtPl947Qh+u/Nm43Vr/vQUcZdvqwUD9JTCKZeOgo1LpPoXCKpu9t2FU7zK+IhBP842rn5kblZMZtfIj1m5oYxPXH20K7o1SkoA3RO82JC32x4MxIfD93M/hdLxYIWIZG404D/w3ux7cXQkM3WGBIv2ESViCR+7qBuWXjrGhbCS9RUutmO4drsrSXVAIChPbIxuHs2zhw9ADcfN9LwrJ1snCcOG9wTL/z+6BBb1M42jdwcNqzZ59eF+ESn/hbn+IxUr+4YZ67zhCE9Q659+9rjHYdWi2UgdSdwE49xJkciLhylpXgst8TsfhNZ2CA73jsz6MRnNkMAgqYN4bQgnUwnWGmgxffFNzu5MG/VnyMRbsJpqu0QHQEHSMyQrMLbOekqGaZFqJtMhVaCo3jcrj3yQ3xRHm6nhP8ufzl9LBoj9F9gZg/yY+LvYTf2c9ty7lz05fnDDaHo7LaO/xzG3IC/E/7/gSazihGCNvD60T3w6qlDAVgrYIbbZBY8Y3BX2ziyVrxz5gGYaEoc0VmYA9JitNsjKyZc0dyU6KDuGa5DwPFFUaqH6GZT43tl4qLhbEzksyf/bZ45MRi8auqloZF3Vl43DouuGouenVIxrGt4G9hwhAtzGXK+dnqk6ZjtMI/5otmD1Xxw5cju2HTjwQBYUo145h2IJR1G+BU1v7IBxTz59O+aiWOH9Q45LxziZGHnKf3s747EQS61mr07Z2BYL6YpFRuYWfNrzgYmHuWT9ZYiJvzuLjeuwEVHApmtMCdb23Y3v8kukm0njlmL2uILxKUDR4RQjZ/+dCbuP4t1ZvE3/OK2U3DVhNCsQnZmGLcdz+wIo5Xt3SzARCYM6YUZ956Dvl2M7fv3RxyAG44ZgQsOHizX/FJqq11yOxGeqoUj8hD5taJ21g7zxCfaxVrVlxdpPm4ezCMJqxqNAvwsi+xPnM7pTPDKduCkZcas+XUTtUEcHmVCebi5jY9NPEpL1072Tlv8d0hP8Ua8Iyba/JojushsfjPTvPjjyUYnoyN6Z+K5kwbh0WPZ9T07peIkIYWu3U7gn8IJv/r/7K+DugfH6CFd0jBdcHh65NiBOEATbmULqUnnjwjxuu+c5sEX5wVtxlti5HCdJZjwPHeiffi8SOnZKQXdMuzb+O3je+Gniw8MG4VDJnjxqd9LCN4/axjuO6yPwS7/Uk0IPmUgG6O4L8Bt43phZLdQ4TbVQyIOVSbD7ZrCjebXLTLNL7+f1SM/ecKgmO8EtgXtr8YRYsxoJNnKu/EknDN2oB4fc3D3LLvTLREHSKtIAYcN6oFDBvbA3zQByyniBCk2fB4NQQ8jc9Ux1vXTelpPTUA2Zw7jdnOvXXUsTrYIC9QzOwNHW8SH7Zxuo/n1hmp+g8+UWNWvpc2vUOceWRmuU+jmSLZqHzx7vOskHLH2tUlP8eK6o0cg1evB/Mmfo3bNNNSumWY455RBnXF032A/+P2BwTiskWiB1lw3DquuHWc76IQTfs3HRc2vVaQMvYVJtjffPmOo/nckZk6ZMUhE8O6kz5C/+CfD+7/6yGEROZvp9TJpfp3GNgeC48xfTh+r7+SIzmbhNDtck8XHXLPjnpXDXkaqNyQCjJ1tu7HOQQ2fWZMm2407uH93XHroUADQ2/6S377HpSO6I1NYOIjCX6rNc4eLSsYP86qI7dhuh0Us96eLD8T8K0ZjQp+skLZxaK9MHCXY+zb7YjOeiqHTYuXwJz7voOw0TL54ZNhwlimEYGyP8CYYshryccFLCIZ0Scc9h/U1jOPje2Vixy2H6AuOjBQP1l8/zhBiMZ641ZTGU868eWxPnNA/WxfuCQEO7tkJt43rhRdPiU9M8kTRYYRfMbKAVR/+vzPGYbwWaF223ecE8bpwjmjRbA+Igyd3jOEDotmpQ6b5ffISFkbJnOb0gbPHY0iPbIzo3UWqLerdOQNPXTpBHzzMwmCOEK2AR2vgmE0IWnyBpDF74BoZ8y8SSZxfADh33EA8eLYQEF14vtMP6u/aYc+t0O2GD599ABXTX0PF9Nf07ygFslK9+OzcoDbp/GE5+t+RhPfpnOZF5zSvbbuXFxt8eea+K2ocrLpbHy0j0sGmMFAUwODOwcVJJGYPVu9h0VVsq9+JhuiP//47Nn39rOH9Z6al6P25r02UAyuG9zK2L/HZ7j6FZfmTpQA2n8v/HiaUF1bzq/WioNOW8R1ZaaHTU7yGcTqnUxreuOY4+5sJ9eTjrfmVG3fjuAkM4+UrjtHb/qR3nwwp955D++CcoUxDbzclOO2fQc/5YGO1myZEIbdvZpruiGd+RnPiRVmqWqfMujyohc5K9aJ/VipSSOSJGew4pFcmememolWzvxVNDkTcvl8RriV344iamWo/TiUD8ahdz06p+Oic4eimhfpjZg8EDx3VHwNikI0ymegwwq8o5Nl1JB4Hsk+XoODmppGJq+NwY4VbzY54ttgx+bYov7ddsfyePD6sWfg96cC+ePe6Eyw7/qe3nGJwuOqRnY7zxwUHrBxhi9Os+TZ/bvH7BXvMxGL1ziKN4fiX08fh9IP62woKz19+FN642tnk3oZRlgAEfw+xr4gxdqOJ9mD3LGK7k4U1M7dLsR5WC6gDczLw08UH4v+OYJocsQixLm40W9wRyMqpK1rzyKy0FGSmpeDf5x+GJy4+Av8871D9mHhHsz3t6L5dMfWes0L6mjjW9NEcIq2dL/W/gotc4bjV2HD0UCZkmDW/5vuYNbNiAH5xPBrbvxt6ZDmzqfR4gja/5vrJNL9c+LSLLgEwIejew5yFurxtXC+cNaQrHj8uNOkBr5JuEiK0G671/P2B3fH0CUbhr7Au6MAlvkbzL2fWnB7Z1948IMtmx2JIl3RcMrybHs5w5uUHYd31B4eYBZ04wFlkiZMHGhdiYk35DlKr9j7M53Kc+rXIzMPuO7wvdtxySFyE90TAHfWsQiEqnNFh3p4YoFrsA69eeazhvMMG9cA/zj0EtxwXaujuBENYHcG5QtTE8MHe7cpSFERSvB7dRCNLMzW45FC2LZFq4yXOy4gkA9QYyUThIQR/Pi04AYt1POYAo810qNlDwLWxf7wxL4ziufo/eEB3jHAQsxWIr+ZXhkykiyTGrgzZsxzXn02kotA4/4rRmH250S7TfKm4OAmYas1TdRIAY3tkSoVbMZlBZxfZsfh2sNWiTd82NH1vdnSygpsjnTCiD7plplv6H1w4PtQOU7ZrJY5LA7sxwcgquoi+C0LkWl6rpnj4EBZOid+KpyU3755kC6ZRXTulBhdaIOjd2ah0cDo85HRKC5o9mCroxBTNjmBSAXseOqo/3jx9KK4aZZ2mmVdF1DlUaZFmnjpxEC4faby2WogmIGrkzeOmuW8O6pyOHbccYlmPt84YKhXSOc+fPFjf9Un3epCe4glR1vz9yP54/6wDLMsAgPOGdkVnm5jTD2uRCLjm12w7OkwzRahrcSj8ClW8dVz4bGTtkRb9XcVvTuDv0S7iT3unwwi/VppfcygtQghOGdnPNiQOJ0fiyCFu8XENS/+umQYnND2MjYOR/dNbTsZ/Lzyc1c107LYTRmFw9yzccwoTPm869kBMvefs0LoL7ZeH9HKbtvTxi4/AS1fIbYmtBLPrjh6Oz289Rf9sngTPHTswLkb7keD1EBwxuAcevfAww/dtLXQmM1nCJNYtAicsjvmNHtyzE149dSimXzbKIMx2y0jB4C7mmJgmwUb4aN4+DqfpoWDbfEO6pOEPB/fCIb0y8fSJg6ShlAAmYJizRVlNDXrfNlXB6WRijvEbrb2lONb065qJX+4+CzcfN1J6rngr2eLPykmVjy18rPP5g2YPd50yGv26svEwLcWDGfeeg6/+cBo+vPFk3H/mwTj6gF4Y1D0Ld540GmfyJDTEuSf8RYcM1hfnZn8E8dn5uOcme6H4DsSoJVbYjRm8LNE+vckmWGyjYAYiDp+8l3TPSMElw7vhj+PdOWf36JSCqw/qIc2oaIW5PxES3lSIEILDehvNdsSnzdHGEf5dmpfgvsOZpv2iYTm4RHNGa3SYJIFXJ81L8I+jnIf4ak9wk5Z4Opk5cbDu2al9R8pt37V3gZjhjYANkEN7RBYQfPyAbvjDCQfhwD5dQmJdigLejccciMU7S9g9ZSGRiHXj5ckyvIRYTpfpKV68d/2J+mdCSNjt1pqmYGKNU0f1wwlh0hMTsIEpojBQHg96Zge1PKLN71d/OA1dOqUaEn0kEkII/nfpkZbHuVY9UmK1frbLchVLZLKBqF0a3Z2ZElz0o7vkLECoNu+Hi5gQ5iR1ptn7WuxXlAIfnT0M/bPtY0oS4XwAmHX5aP3Y5Qd2xzlDu6JfVipeX1tiuK5buhcZmrQtpg2df8VoeAjB8ZOCsbMjic8qYhZ2CSF4+YpjcO9XSxw3po9uOgk3fjgPgCn0GyHw2AwUoqmDXPMrv/bcsQPR3OrHJZojma759Xhw8SFDMLZfN/zpi0U4RPOr4P4BI/t0xWMXMR+EVC/bMfp1cwGrp4Nx58QRfeD1eDCyDzP5MGu+xXE20uxznFMHdcGmGw/GmI/Wu7qOvzNeE1H4PcrGRMEo/Bp/QwDomubF8ye7j8LAy7pkRDecM7Qr6s1GwxLMbdIDElZ54SHAjWN6otlP8cyKQgD28Z/TPAQDNBt9DwmGJmtwKPzy99seVBavnzYEs/NqXF/X3AaaXyfM/N1BUdmWJ5oOo/m98shBum0sIQSPXXQEbj3emWmD2S5sQE4WRvXtKh2YL9K2Ift0ztCzallpe8y7k+eOC25D6SkXxXvEoK2LDiUPnXMITrTIKGbGLuavFWbNttEkxON4cks0M+49B386eXT4EyXE8umev/woS+17rBnaJXRXQ/R89xKia0GtWH7NWCy+KjTGazRKzDHdO2Hp1fK4sQFKccKAzmFjb4bb0stO9er2wYbyEdS26IIABfpnp6FvllHg5sfPHGzcWZrQx94WkyPbFfLq2mZnklsfIdlJuF2m648eof+tK62JhebXoqge2Rm49YRR+s7TFUccYKjHiN5d8Nktp+D8g+VOTZxjhvXGBQcPctznRFlKZvLhE+xF9XfoQvo1v28nGjezycHZWlg7/jq5je7Ibhm22uQGQSgVteDRyj2iIJ2R4kEPB1q8EOFX0PyOkoQE4+cQQsKa+7x0ymAc2ScLhBCcMbgrDu2VibsP7aMLv841v8k/n3DOGZqDpyMIH8cjomQkOLxY5zQvetqENk12Oozmt3eXDBzUNwcr9pS5nnwvGj8YWWkpeGYGW+3bmQzozmCE6FsHlBo1KLqDh0nz26ez6I2uBeQnRD8/0m7NB+/DBvXANUcND3O2+VpG9yzroOpWmCdOQ6g2/TvXxbZL3Ey2Ii9dcTSatQnw4AHWtoSx5NNzhxtCnHFEza/XQ8IKVN0tYne6jVlsFG6AHItweubdY75NG5qcwN39V107Fkd8thEPTOiHzzaXAQBaNeFFFIy+v/BAlDX69HsvuHI0umek4JddVfo5jxw7AAv21aKgPjQTFcAExO0lNejbNTQRiJO+QiTaQcBa+L39xFFI83px0SGD8cnS7SFlyLI7OTVFOGP0AJxhsivu1Tm8dj/V68GfTxvr6B6AMRKFDDF2sNNsVTIijbX908UH6mG6uLDIZbkB2akGcyIzw3OCWebEZhytkBfJAtQc6o0pL9jfVqEPeT8RLz20VyaWFtXjbiE28oXDuuHCYczEoUu6F99eeCAAYF0ZyzTY4EAzLbI/TyttqflNdBSmeNJhhF9ADA7trtEQQnQnEQC4+bgDbc7V/jd/L3zD25NV6JyrJgzDpBU79XNknvfh6N05AyW1TQCCzm13nHiQbQY2O7rbpBY1c/3RI/DJ0u36wNi/ayYKqhsM5/BniSZlaLsgyucb009ufxpPju1nNAd67qRBOLin0WHM68Dez4pwl900pqflObJ7fnfhgbhs8rYQDd0t43qhpsUfkhCA43Rg75qegu2aJu/bbRUAgk4nYhHjTalg+2WFasbTvR5Mv2yUPoGZuWrCMIzq09WgteVEKnwB1qEbLz88VOt4/dEj8OHibTh2WB9M35RvWY+7TxmN1XkVWLijOOJ6Rcutx4+UPoOIuHAPZqtyPqtna8KpOVSeU0Qb6eFaJrdj+2VjcWEdTuhvL7g/d9JgHPrpBlaOYWETUVWiwryA8pDwW8elDWwxKF75f0f0wwXDumGMJNOjGa4xNvctK3RF0X48r3AnwKE2Wf6i5c3Th+L9jaXok9l+Nbvh6FDCbzDcjPtr+VA5qk9XPbqC/B5cwAayNc3XhCE9cdlhQ1Fc24jimkbcfCyzcRQ1v/efOQ5ldc36tbpTHCERrb4+vvlk3R754XMPwYzN+3BAT/c2zveeNhaT1+0NccCx4/pjRuD6Y4LbqK9ceYz+bBx9kbAfD1LtkckXhzpBXToiVOPscWBfbkW4/vevY4zawszUUJtNEW5yYLan7ZTiwUMSp5doWhzXfnNbt0gSaWWmemE1p3gIpIKviHk8mDCkJ1bsKbM8v0dWuquFysBuWXj1qmO1+libPVx0yBBcdMgQnPXytJBz2oqRfbqG3YEY2qMzPrjxRPTvmon1+yoBuPvdemem4rsLD7Tc2g+HWL2rR/XA8K4ZOKpvFq4Y2T2s05BVBJJI2nB2qgd1LjWoImaHNw8BemoN+bDeWVhf1mg4ftu4XvidlhjHHFLQieALAGN6dMJvlx+EwZ2dxZhN93qQ7iX459H7p7MbANwwpicO753leEEQCeN7ZeKl/SyphZkOJfxGmrjCjv9dMgHFtY146beNAIxpLLtkpOGTm09Gj6x0pHg9ITFdxUH7rDED8fmyHQCMk5vHsAXpvF78uj6dM9AjOwNXH+nO3IFz/sGDwtrohaNLpzR0MUXG4I/SHmx+OxLhJqXBndOwt7YFXhJ5BAJREzblUnnEAZGLhnXD/fPy2LWSW4rOZ7Hkt8sPCpnwj+yThZl7azStbn3MAwFFshh84uIjsKGgEn/9ZlnIsed+dxQGdMvE9I37HJcXbsxJ5IL1hcuPRnqqB+/Mz8Xa/ArHQuCAHLZzx9OvD+nuThFwiIWg0SXNi5oWeUpmAk2JYTJXOFrbWenlUKsWrdDKWXv9wTj1683YWxuZk7E5iJCHEAzqnIZfLhmJYV3T8fEm4wJMXHjyNhVJfxnaxbmG0+sh2HTj+PAntmM8hMRV8O0odCjh1xPFJMnD9xw33BhS5oghPQFAF37NwpydFsc8kcsGcmZfF9kU+8Mfz4gqRWo80b2fk7N6MSeZTadOPuM8LNtV6ujc3pmp2FvbgmY/DWnrI3LScUCXdPy6196DWbzMifOQIUmBRPDiQrib8FVA+N9ENuneOq4XTh7YBYsKa/HDjsqIbblFzj/1DCwtbkB9c2tIunERK5mT2DiOjh/ING9dXDimhA11lsA+O24AMwOK9L0P7dEZz1x2pCFm+alnnA8AyPQ3W1wVyq3jemFAdiquGNnDst15CeCj1qHhnDLl0lHYXNEoPdaW65CQCCTa/wd1D6/F7SDDvKIdEZXwSwh5FsCFAFoA7ABwM6W0Kgb1igvcUcMfgfTbMzsD395xuh4xwsyHN56E9FQPKuqDpgvhcKJB8QhmD24HUbuJNNF0FM3vaC2O9Kkj2yZPfCS8/dGPONsUss8Kvk1b1cy0XSO7ZWBgdhpm5dVgaJd0vHXGARj+/lrbMsTf3K0MI9f8EldlRdPkCCE4sFsGFhfWubqnHT++9QFO/WE79lbUW44v0XLu2EEIBIDzxlknNuAYNL+SMae9myodOqiH4fNbH/8IABheW+i4DCcxZAkhAKVRLxYGZKeFpJaNtNmN7JaBvbUtESWsMS88zc1gcOc03Dm+N/6xUGIn3r6bjGI/JNqR9lcAD1FKfYSQpwE8BODB6KsVH7jw6FZDxLFzFuufw7YhqhpiG7fWI8b5TfIBZFx/585ZHcXmd0BOFmbce06iq2GLm9/gkWMGoHOqF6doaUinXjoKzf4A/rNonx6cPhxG+z9XVbWIl83+97eha3KsW22DtnWebeNPYMeI3l0wpl+OZXgwr4fgokOchVUSoznIBDfzd/8671Ck20Qs6KgEx7jE1kPk+ZMHY31ZI3pH4MgUzsxp9u9Z25ubXxtiJrK/j/OK9kdUwi+ldIbwcQmAy6OrTnzhg3ostirDEavO3l7MAtwKeB3N7GF/oVdmKp460WgDnu71hHxnx58P64MVxfW44+DeGNQ5eo9lrvl1ahXJm1w0wwAP7WQV4knk8gO74ZttlbbnNLYwr/isCDW/6SnemMWANoQWl4xj5u+cxgrvaLB2SaOK0hFrslO9IdFcnOLUxv+N04eGfNdhEgoo2g2xbJO3AJgaw/Jijj5JxlH45VuG0Q53vztsKCsnBnF+kxHdMVBpBJKCF04ejCmXhHc+iwWjunXC0qvH4pZx8hBkbkn1EvTNSsUTx4Xf0gdio4m77MDu+OP43rjXgbb7n0cPCHvOqaOYWUyWjalS/64s3NwNQiSVeCBmYtTlHWHIVD3WGUHTrvjfoy0Ily7cDjXMK5KNsGoGQshMALIR/mFK6Y/aOQ8D8AH4zKac2wHcDgCDB7vPahILorH5dUosOjkFxR0nHYQ7TjrIVPb+M4LsT8/S3nn1uUfRraUOawD8557/S3R1XOMhBAuvlGd9s8NppjQZqR6C+yc4s+MO19QfffUF0NRsnB0IwOux3kHplJaCKfec7aaaEZHiFYXf/c/m18yrzz0KAOjWUhfT9q9nyotZiaG0pSOt2XnajUa7PaUdVnQMwgq/lNIz7I4TQm4EcAGA06mNPQGl9B0A7wDAhAkTEuL8zsfsOMq+IfdyQ08t+1GvbKP3rJ7kIso6JSu3Hj8Shw/uEf5ERVx47YXH9L/bo/DrlrbuR+Fi7D722ov633998NF4Vycs4vZ2sgq6saxXvNq/Rzftiv077KHFkD93aNcwZ8aOlGgeI0nbkaLjEm20h3PAHNxOppQ2hDs/0bSF2QMvOhI7rzMO6o/s9FQcfYBxO5gGC90vuXLCsERXQdHOWHTVGNRZxFd1SlutwNubvaMxRXICK9LO4e8uHu+wW0YKVl87Dtlpbde6zAuObhnOnRxVO1IkG9FGe3gNQDqAX7WOsYRSemfUtYoTnjYQfnUi6OyEEBw7rHfI9zmdmFNQ/64qsLWiY/L6aUOworhe/9wnMzXi1Js9tZi3AyTph+OBpx3P/Mmq+W0PxPtn75IeXvjs3SkFJY2+mN736lE98PjxzuzrOVxET+Z454qORbTRHuLreRFj+CQUiKPdg1sl7ei+XUPiTpo5bHAPPH7REfuFaUCfzhkorm1KdDUU7YxzhubgnKE5MSnrlIGd8dbpQ3HqoC4xKS8c7U3zK8IFuGQTWtoiYk+08N2/RMYyn/X70fjvkn3o5kBQdsKOWw6J6Dq1iFIkG8mbBSEO8IE8nvFAuRON067+8pXHOjrvqANi4xmfaF6+8ljsrahLdDUUcWTyxSORlZq8Ih8hBGcOaTtbyXas+NXxECFdbxszqFsWhvXqnIA7R4fu8JbA379Tigf/OyG69PQKxf5IhxJ+e3dmjmQ5neK/3alWunK6Z6Wje1b0sV0VycuYHuHTnXYk2vNY0Opn4m5aihcTbzgR20vtU1fHg4k3nBjyXXt4p1z4bRMzO4VC4YoOJfxefMgQ9MzOwIkj+sTtHmqcUygUMk5rIzOLWNLiZ06FaV4PBnbLwsBuWQmuEePuU8bg7flbMKZfTqKrYgkX0NWcoFAkHx1K+PV6CE5qo2xE7UAxoVAo2oglV41x5KCUbLT6Wd68NLd5qOPMkB7ZePKSCYmuhi3KyUuhSF46lPDbFgRtfpX0q1AoGL0ijEyRaFp9XPhtf4J7ouGObvFMqqRQKCIjuZbz+wHZ6WySG9mn/W1xKhQKhUgL1/x61VThln8e3R89MlIiDsmnUCjih9L8xph+XTPx2lXHYmiP9uedrOiYXHHtbejSmvQ5avZbbrviGtSkuovhferIfthWUh2nGgVp8e3/wu8V194GADHvA2cO6dqmUUUUCoVzlPAbB0b2UQOeov3w2LNvYXhtYaKr0WF567GnsaNzP1fXPHRuZPFW7Xj5imNCIhP01lKu98/ZfxPsPPbsWwCg+kAcUUaAimRDCb8KhUKhwGhJ5ITzDh6EAd2ycOjA7m1fIYVCoYgTSvhVKBQKhRQPITgsTAZKhUKhaG/sv4ZcCoVCoVAoFAqFCaX5VSg6OP/62526s89bjz2d4Np0PO7814O6wxu3P1W0Hf/6250AmMObav8KRceA0ASkn5kwYQJdsWJFm993a6G/ze+pUCQ7o/oH18C+3LwE1qRjkjJqkP53boEvgTXpmKj2H3/WljbgssnbcHDPTvjhopGJro6ijfGOHJiQ+xJCVlJKpdlwlNmDQqFQKBQKhaLDoIRfhUKhUCgUcUOFOlMkG0r4VSgUCoVCoVB0GJTwq1AoFAqFIm7wBIFZqd7EVkSh0IiJ8EsIuZ8QQgkhPWNRnkKhUCgUiv2DMd074YEJ/fDSyYMTXRWFAkAMQp0RQgYBOBPA3uiro1AoFAqFYn+CEII7xvdOdDUUCp1YaH5fBPAAgLaPmaZQKBQKhUKhULggKuGXEHIRgH2U0rUOzr2dELKCELKitLQ0mtsqFAqFQqFQKBQREdbsgRAyE0BfyaGHAfwDwFlObkQpfQfAOwBLcuGijgqFQqFQKBQKRUyIOMMbIeRgAL8BaNC+GgigAMBRlNKiMNeWAtgT0Y2joyeAsgTct72i3pc71Ptyh3pf7lDvyznqXblDvS93qPfljkS9ryGU0l6yAzFLb0wI2Q1gAqU0aRsEIWSFVao7RSjqfblDvS93qPflDvW+nKPelTvU+3KHel/uSMb3peL8KhQKhUKhUCg6DFGHOuNQSofGqiyFQqFQKBQKhSIedDTN7zuJrkA7Q70vd6j35Q71vtyh3pdz1Ltyh3pf7lDvyx1J975iZvOrUCgUCoVCoVAkOx1N86tQKBQKhUKh6MAo4VehUCgUCoVC0WFQwq9CoVAoFAqFosOghF+FQqFQKBQKRYdBCb8KhUKhUCgUig6DEn4VCoVCoVAoFB0GJfwqFAqFQqFQKDoMSvhVKBQKhUKhUHQYlPCrUCgUCoVCoegwKOFXoVAoFAqFQtFhUMKvQqFQKBQKhaLDoIRfhUKhUCgUCkWHISURN+3ZsycdOnRom9+3qbXNb6lQJD0lxQVICfgBAP1790lwbToeBSXF8Hm8AIDeffonuDYdj5LiAgBASsCv2r9CEQ8y0hJy25UrV5ZRSnvJjhFKaVvXBxMmTKArVqxo8/tuLfS3+T0VimRnVP/gGtiXm5fAmnRMUkYN0v/OLfAlsCYdE9X+FYr44h05MCH3JYSspJROkB1TZg8KhUKhUCgUig6DEn4VCoVCoVAoFB0GJfwqFAqFQhElzb4A5ubXJLoaCoXCAQlxeFMoFMnDt9OWYmB9WaKr0WFZ+u0vyM/qmehqdFi+nbYUAKLuA/9dug9f5lbgp4sPxNgembGomkKhiBNK+FUoOjjjxh+B4bWFia5Gh+WIceOR07lfoqvRYRk3/ggAiLoP7KpuBgDUtASirpNCoYgvyuxBoVAoFAqFQtFhUMKvQqFQKBQKhaLDoIRfhUKhUCgUCkWHQdn8KhQdHBXkP7GoJBeJRbV/haLjETPNLyHESwhZTQj5OVZlKhQKhUKhUCgUsSSWZg/3Atgcw/IUCoVCoVAoFIqYEhPhlxAyEMD5AN6LRXkKhUKhULRHSKIroFAowhIrze9LAB4AYBngkBByOyFkBSFkRWlpaYxuq1AoFApF8kATXQGFQhGWqIVfQsgFAEoopSvtzqOUvkMpnUApndCrV69ob6tQKBQKhUKhULgmFprf4wFcRAjZDeBLAKcRQj6NQbkKhUKhULQrlNmDQpH8RC38UkofopQOpJQOBXAVgFmU0uuirplCoVAoFAqFwjWz82rw6priRFcjaVFJLhQKhWsopfhpRyVa/JZm/gqFIsE0tPox/P21+GpreaKromhjbvt1F15aVZToaiQtMRV+KaVzKKUXxLJMhUKRfMzYU4O/zN2L19eWJLoqCkVSEG9HN1+AojXg7i7FDSxpypuqnyoUBpTmV6FQuKaiiU2qpQ2tCa5JYlhb2qC03oo25eSvN+Pgj9e7uoYk0AB5Z3UTnlpeAEpV/AtF8qHSGysUHZx5q/ZiaJ0727CANqF5Ejm7Johd1c24bPI2XD+6Bx45dmDU5e2dtxy7s/vEoGaKSJi3ai8AuO4DbU1RfftaaN4yfRfy6lpw/eieGJCdlujqKBQGlPCrUHRw+vTtj/617oRYvvvq3Y9l3++3V+Dp5YVYdNUYg5DPtd4byhpjcp/+ffqisXO/mJSlcE+fvv0BwHUfAJgpQopn/+oEAUqxqbwR43pmRlVOq7ZA3r/eTnwprG+BL0AxqHN6oquy36PMHhQKhWsCmoXjp1sS40jz3IpCLCuqi+s9/rkwH6WNPjT7jdu2HVDZ3SH5ems5apr9mL67GsPfX6svejjTd1dj1IfrsLWyKUE1jA9vrSvBxT9tw+qS+ugKUtYOrjlh0mac8vWWRFejQ6CEX4VC4RqXfjcx5811Jbh6yo643oNrewMWNovitw/O34vh76+Na30UbcfG8gb8fUE+HlyQh482sYykWyqMmv4Ze6oBAOvLGgzft/e1Ed/RaG9mFsnAy6uKMHGDymDLeW5FIQ78IDnHRSX8KhQdnOKiAhQUF6Gg2HlYnLYWfndVN2Nufk1E19a2+FHb4nd9Hd/NNj8rF25EmfibbZUR1Q0ACoqLUFxUgOKigojLUEQOf/di+29oZc6MZY2tSNUags+i0ZuF3WRSeEYjiPMdjkeX7MOKOO+yxJKfd1Zi+PtrUd/qvs9HyytrivHkMvt+/OqaYjy8MK+NamQPpRTfbquIm/Pum+tKEq4osULZ/CoUHZyTDh+s/+3LdTYoW2lD48UZ37KtwB23HOL62kM/3RDxtQDgNz2rLvzGSMwZfNKR+t+5BT6bMxXxQNb+gzbtRLfp9bXD4B68hbrprvxUAiYcfbypDB9vKnPdf3j/kJkJVTb5cM73uXj3zAMwPgrb4p3VTTigSzqIcJPXtMQO++paMbKbN+Ky4wWPvfvE8YMSXBPg511VeGB+HgrqWnDPYX0TXZ02RWl+FQpFWP70227c/usu/XO41TylFM+uKAzZErbj7XUlOPf73EirGHP4fGrW+PGJNkkVGooY4NejmUAXfp3G2E0ms4dwQm9Nsx8ri422vcGFLYmqjfNiZHVYUliHskYf3ooi/vCaknqc+W0uPtpUBgA46vONuGPmrg7ZL3fXNOPNte6jlZQ3ssV2RXPba8kTjRJ+XRCgFD+s2YNmX9s1lF1ltWhoUdogRWKZvqcav+UFzQ7CaX79lDnO/G7yNsf3eGZFoSPnITFu6FtrizHeZexTp3g0McZvYfag2P/IrWhEa4DqApuXEEuzByvNZjIJX+H66S0zduKKX7ZLTToIic68iZr+lx2Lxnl0d00LABZzGwDKm3yYuTcy06j2zo3TduK5lUX4bW91SOx1uzbAx7aUDjioKeHXBXO3FuKNuZvxyZLtbXbPOz5biH/+uLLN7qdQOEGcFGVB7M2mAvG697Mri1Afp/1obvPrt5AAVOz+/Y/zftiKqbuqwFuUh0AXfs2aX/77E9NyKBYmQcuL6vR2Z2Vr7ATx0gClyDU57fGdGbHOweeKjSBv9z5iIXM5FaBb/AE0tkfbFQfw57p95m5c8pNR4WBevIvwtuXdz8L1OUEJvy5o0JxmapvaxguWCxUbCiJ3plEo4oE4hcgGVyuBMdb3jifcvCFEkNfmCSX7Rs+WoirMzi1MdDUMVDT59PZLDDa/7LvKJh+Gv78WCwvqtHPYdfo2f5T3X1pYh6um7MCb60qwr64Foz5ch2+2VURUFg9JSMHMis77YavBFEk34REXs/qx6AR5fqVsKIjFwjGczb1ZnLvox60YF6ddokQjxlsvMmt+bcZi3qZTOmD8RiX8JjHJ6iWpUIgDqkzQ9QvaI7esL2vA3+bttZx4nUzILf6ArQfzx5vKcN1U+1BpvO5mZVEyTBNnvTwNHy12blKSrPx50hL8b1pyhUJK8QRtXb0Sm991ZcFtdinaxetKG1AXQcQBLrxsr2rCNs0M6JedVa7LAYBZghkANw8oqAsKR7wti01cN+dAdEIqv1Yq/Ar3iLZ8s+bdim1VzVHcLT5M312FPTXR18su06Z5FFxZXI+d1axd8YV9tIlahr+/Fg8tSI4IFk5Rwq8L9JVmG81+Kid6+2TRjmI0trZvO+2JG0qRW2mdwUwcUH02Zg8kAo3C7b/uwnfbK3VnjJB7O+gWh366Acd+ucny+KNL9mFxIdPcLS6skzoz8apbRXuoaPLh/nl70ZCAkEoA8Nmy+MY57qgQGNN3m21+b5mxy+pSAMAN03eiptmPSydvw72z90RVj2hngOdWssgC1EpPqofzk5k9ROnwpv2/uSJ0HNHLjdFc+vXWxCTbsWN3TTN2hxFs/zRrD87TnHyjCc1mHmarmoNjp3n8uuKX7TjzW3bPWJo9fLU1st2JRKGE3wgwNxN/IBCXbd62DieliJ4dpTV45OfVeHW2teCV7FBK8eSyAlz0w1bLc/wGza/sOPs/kiFV1xpbXCzrF+aFYrOfosqBB/OqknpcN3UHXlwZuvXOB0d/gGJjeQM2lRsn8cL6Vny/vRLfbW8fZkm7y2txy0fzUNPUEtNym31+FFQ5j+qR7BASbIN2Nr/6+ZLvShqZdnVOfi2O+GwDtle5zwIXr9Ff7Fe65jcOZg+8pHskCwC3Wlvr0hkvrAzGaJZVWbYLVN7ow2ebyyK+fzhO/2YLTv8mfLa2Jq2xPbsiOAZRSsMKziJm2fWIzzbqfwds7MTayuEtGRV5Svh1g8Xvd/0Hc3Ht+3NifruAPkAo2gv1WmSOomprrWmyw9udz2a8Eg/ZaX4jabxcR2XlmyLfRo2M0gb2e+2sDp1oPLrNL3DRj9tw4Y9sMWAWytu6f0YqkHyxfCfyqxqwfHdsJ/wnp67FTR/Ng08QMCilKKxurwIx0SdrDyG6VsxHaUiWNw6l1CDgtgqG8FXNfnzuIg24rD3N21dr+Fzf6g/bDpwIHEHhN1TzC8Tfrj1WDm8eieYyINT+3fXGrGvNvgCO+mIj/r14X8zSUzt535vLreeF2pZg//l+eyVO/2YLFhbUWp4v4rF5kwX11otdK83vI4vz8Z9F+Y7u7YQklH2V8BsLyuqaUVEfe3siqm8dx7xohcISmTBrRtxK+8/ifOyoajIIBlbRHvJqmzFzb3XwPIkkqwvf2h9mDYhc8xu2ylK4oC21meNxfkPMHqw7ZDw0HJUNzahrDtppivbWS3e5iJMapwlo+W4mWIg/5Q9r9+DGD+dha3G1xVX2NLb68MGirWhpw7CSnNl5NXhrHXuvHmIU0F5eLY+l+vOuKlQKOw2NMciYZdWUdlQ1YfwnG/DiKvuMjG42I2WaXw+Jjc0vK99YkKhdBphmtsLKhtpEiz+AG6btwNz8oGAo65HiLc1OYC8I7645RtnN3t8YflE5ycI8wzwOcrty2Y7Bnppm1Jh2texkhN//bB2dSrf5NRXwyeZyfCpZsLX4A1hdUh/yvQzRsTIZd7GV8JvEKIc3RSJwYsIjnjJlVzXO+i4X52tholgZ7Jh5TD7v+624Y+ZuAMA/F+Zj5Ifr8MrqIsNgzsvm28zmrUNZ9cTpa43DwVksSzZ5iGYPTolHl73y3dm4ZuIc/bM4kfzrp1Wuy4vXWlq0Kt1YUAUAEWt/P1+2A18s34lfNwdTxVJKUd8c/0g7s/JqsK6MLeTMCsUMr/ztiU5kAEJCasWyXZz1HbPXnJtfi/pWv6Xw5rfQ5opPII9oEnRGsxNaShpadYc8GeKV4RKE/Hn2Hhz5+UbbczjLiuqxsKAOv2hjjVV7Fp9JtKc95JP1Bm1orObZ6burwp6T7pWLXFYpkc2vvzVAcdo3W3Dnb0a7czuT3QahLebXGrXAQc2v9fUiTy0vxOU/b7c046lp9qOu1Y+yxlZDyLVkFGWU8OsCfbUqdLd4aHpafH787dtl2FbCtSZK9dsWfLp0O96cuzm6QpKxl4cht8AHX26entrVLi4kx2oy4+YDVppfcSD+IpdpFl5eXYwbpwedt/iE2xII4M6Zoc5FUuFXuN/vbDQdVtfJBkLd7MEiuQFnb234iZRSiiYLOw5fbh5yC3y2qY2bWv1o9QewuagqYv+CWKVjDi2XEUvtzj7NhjgrPUX/7uuVu3DpW7+hvC4229Qc/u7/NnFRyDHzdnJGSmhLIQTok5lq+E4WT7ayyRfTd7SxvBHjP9mAi38MjfpR0tCKX/cYEz7Ibk0kx5yaPZw4aRPO+T5XHwveXFtssIsXywlJEKJ93FLRiFO/3oxfXSSnqG81vlsr22TxluI1da0BVDaJi+3Y/CZWERNEobhScEQTZYcpu6ogYuUozOM0b6kw9gG7aA8iN03fafgcbqxv9gXwzdYKva5cm1vV7MeN03aEhOA77LMNOOaLTfjN9Hvul5pfQsggQshsQshmQshGQsi9sahYMiO2s6Y4bMttLa7B2vwK3WlKmT04p7C6Qd+Gdcp/f16N9xduxcdLtuP7NZF7ZwP2+ezbgvrm1qhtLe20NG+tLcZN03dYhhF7YVURShpag9EetO+Hv78WzwtOZeYtTq5pA4KTVm1LIGRSpJRGbPZAKcXw99ca0oAGNb+hPxj/xjxBmF/PextKhWMUu6qbMfz9tQZB4NPN5Rj78XoU1AUF5dyKRlcJDN6Ysxn3TlqiC4ZW+PwBqbmBeas51tg51rilqoG9p06pXv27JbvYew73/JEia0NmeSZdovklADqZPIYaTAJaWUMrJny+Ea9amE1I6wPgpx2VYc/bJtHCXTd1B/48JziWiY8mc3jzSwReDyGGtm5uq9wnYNxH69DkC+C5lUW4Zqp84WkeLvg4ua2q2bB4dIJsYS1fEAf/NgvM4hpGJgAGKHWdEMNK+P3TLPY7LC+qw7fbgr+n1W8iYq4aVx50TfcavrfYkAih2pQtlv+mVsPQcysL8eCCPMzOqzWc5yXAgoI6PDg/NLxZoy+Afyw02gsnoewbE82vD8BfKaWjARwD4C5CyJgYlJt0yH7AePyofGAIapqTi/pmH37bIt+mSTQ3fzQfD2sZ8RpbfHjptw1ht0oX7CjGlyt22p7jlrb6zSilmJ1bAL8medwzaQlu/HBeVGWKmsWyxlb8de5e/fOzK4swf18dWmxUBqtLGoJmD8Ko/sbaoH1qpY19Hx/gZdu5ASpPcuGkG/Iqi/Z+fCKVzVu86qLNL6X2+lMK4FfNpvmH7ZU4+avNuHbqDvysaXby61rQGmBC+Hk/bMVLYew2RTYUsokzXLrzdxfk4u4vFyOvsi60cgBi3TqD8VxjNxhyTZPYFrkgXB9Buvf8yvqwyTRkv6yoUaPUetva3B3MglOpFrZvxh7nNtCVTT78FGF83/w6ZwIlfzz+21FKDUKu+JOO+nCddLHmo8GYx6LtqHjmhT9uNWhArZqKk51UWehBmfD+59l7UKLZ+pp3XbxCPWXt9uXVxRj38XrUtjhXbolliqHGOOICH6Y6E0IcLUq5YsIsaDsNKSkOqdsqm1CnPZ/VbhK3lW7QlHzB8dLd7vd+KfxSSgsppau0v2sBbAYwINpy2wvxMHvQS0xSh7cXf9uAp6evw47S5MujLg5k367ejSkb8vHd6ui0uW5o604+K7cQ/5u2Dt+s2g2ATfLRIgp7R3+xCT9ItE92wi8LFRXU/Mr6iJOUxM2ScBMjP1wnnVj47/6rSbgQNWe8NNl2rJ3wK04Mjb6AraRNaXBQDYAiv64FSwrrsKKY/S7/XrTPEFZtlQv75GZutxhmPMjVtL5biqqxeGeoQ1y8xpNYjoUeU2xdAMjUTCAaIxB+b/tkQdhkGla1D7Ybqoc9M2MWyBpM7ZtfFs/shM3+AF5cVYTX1hSj2YntkgA/++8L8rC0qF7/zrwgmGph18pjcucIZiritfl1Lbhvzl7hmBwnGyGyTSdZ28uva8EzWvgwOyde2av6fjvbzv/jb7sdad8BIDs1KE4dJ4kxbl7Mi3OVky75/oZS7NbMysyaXqddWmyn53yfiym7q7XvLc7XqswFe/77iO/bSUuLl8lVNKSEP8U5hJChAA4DsDSW5SYz8XBK4w3LXdeIjs2FVbj3qyX46KaT0K9rpu25ZZrNXVOCgvs7hXvIZ6Qat4iem7EendK8uOuU+G1QRJLcIRIqG5oN/0fChnUrUVXPPJWPGDdeOrmYabFp+EwTEzwuO9W8LSzDatsxtyJ0m5ffgyeu4PxF0FqH9iuAV0MWwYHbe4oTw56alrCaXx42SDbfbqtqMmxTUwArN6xDfhbbSRk3/gj9WE1ji2FHokV7H61hFg78OZ+dwVK5zrj3HP1e8cTKzjsSgvbWwWfNTGPTVTjNtww7rfSGdWynqHj3FgB9LM+jMAo4HAIS8uzmbXb+PI7Cj5Hg/dywtLAOr62xNquwK48v8L4Rt+UpDem7983ZiwuHdQu5nmt+u6QFx1q7R7U6NO7j9Zh7xWj4KcW+ulZM6JMVco48tKK8PK65NT+H+FGW/pcK48niwjpcNDz0mUUK6lqQk8Hap5dAuvgwfyc6+RbWt6JZ6Nfm0ai62YcnBKc4s42vkxwVtS1+1FmMu1b9w6zp5e2kUXgWJ+usZHTej5nwSwjJBvAtgPsopSEqQULI7QBuB4DBgwfH6rZtjKyTxEHzS43/t4UYNW0Ts9FZtbcc5x9sL/zKYkMmI/WahlB0mgGAGZv3AUBchd+2gg8qTh0eZPzunKP1v325eY5CndmlDgaAn7XtWrMgzHEk/EYQgshuEpA9Fh/MpdEeuOZXuHBXTTP6Z6WGnqwh9gkntQ9Q4Ojfna9/Fp3e3pq/BTOFaAfcvyDcuw/368V+POFb5rErkbdnUfObppkctMYoNBVHbP9DHvzZcExsFwEqd3gDQreNzQu34JiJkPN+2lGJK0Z2ByEEq0vqdQ2pm/G1xR/Ao0v2OTrXGO2B/V9Q3xpi69/gC2Cvw0QL/HnT7bIlOGh4LQGKufk1eHRJARp9Aey45RCsKanH0qJ63DG+N4DQ90JALN9VjS78Go+LH2U+DuHefG2LHyUNrRiekwEAOPGroKN0pxRPiJC5sbwhxPTicpNjLtfCyjAvzs2aXyfj/4lfWSdeshJgdRtfj/G8RuH55uaH3wFOxiQXMRF+CSGpYILvZ5TS72TnUErfAfAOAEyYMCH53oQLiMFeKPblm4sU7bII4qNV5FouZ5oJa82WjH2V9dhVXocTRlhrVeIBtw3MSovpBoctbd2wA/q2fWzaxPD31+KWsT3Dnme3rbqosA4fbwrGvJRNLg0OHEWtoiPIaA1QtAaobbB3WY19umYj9Bh/p6I2rdkfsBVqAzRYlpO+ZCfg+E3vmMe8DRf7Nhp7SoD11y6d0tA5w1rIlxHLxTB/hz7hHejKgDa0AwtQqmvkVhTXhySb0M8zfTanqg2O4cbznl5egE82l6Os0QdfgBpi17qxXHhxVRF211jb+cpMGICgUHXt1NBU2XfNcm4uxs08rMxCpBWyQVw88Ogtfzi4F15aVWSwrQXYuz2oeyfdtEhshly7an6X4rtwKvw2+QL4aFMZbhnXC1dP2Y7NFU3YccshIefJZIKLftyG60f3MHxnZ5fNn/DxpQWY0CcLQ7ukG45vqmhCiz+gLwidaX6tRy7R5nttadChlC/8+Tvnn8Xx+y+COYt1+eHr19bEItoDATARwGZK6QvRV6l94XRCmbetyLk9Jtf8mlzeznllOl6YucFlDZ3BbeysGmlhdYM+6Qa35YIn5xZV44vloQMoANz88Xz895fVruu0Lr8iqhUj3x5NS/GGOTOGuKzulqIqlNRGng1OzEQVK5xko7LzhBad2QgBxn8S2mbNNpFu7iF7xQ/Oz8NBH65Dk41WUCac8YlPJjRzz/71ZWLyjvCLPgLni0O7c8xZlzjNLr3QzYQTHm/+eD7u+iI07JcV8XB4k5k9BGwWKlY0tPgwbWPkmap+3FGlB/u3EnzFuun3Ncf55e/I1HpLtAyDL6wqwitritEiPK+bkHbvrLePcFNU3ypkDA2+wEhGDdmYzBeqfsqScLDzrMswvweRvy8I/l5iPNltVU14fW0JXpGYdnTToh+kmRpHXWsANc2h2fDCaX5lTNxQimdWFOKLLeXYLDG94liNbW7tsDnXTtmBZUV1Id//LDhD2i36ncCr9v32SkOoSL4YM++CiRFunJCMmt9YRHs4HsD1AE4jhKzR/p0Xg3LbBXadWOTxKWtwy8fzHZ1LTduI4lw1fZOzrS236A4Z2k1bfH6c9fI0/Lp5H1p8Adz44Tw8PX2ddm6ooHzPpMX4YFFovMlIWbSjGPd/uww/rQu/qrRCZsslssxlSDQ3OB2K/jxpCW74IPLoDLpjmemG0Qgidva8HJnTGSc7Nfxiw2wTKcNK8yt7tFl5bOvNzjv7/+aGtiWfhdlDbYtfGrooECbaQ4DSoObX5rzg+dbHUkyRBbgw3OIPp/mVF+qmRRTVGBdk9321BH/7dpntNWKbizYWr8ekaQIEczAX5bw6e5NBYRAPU605+TV4aIFRwDab9fDnMN/eXB9RO7e6NLYh3eokfSOSNbMs7TlfqC4prMNZ3+ViXWmDrVOr05/hhmlBhUonm0wMfAenJUANc3J+XQsO+2wDtlcZzTfE20uFX5mJlHaPcmFxHzBFx7BDzGzphnpfALdriYFExNu6WRDK4O1QluYdYIurL3PL9XCGokLAydMnoewbk2gPCyilhFI6nlJ6qPZvSiwql2zIfr+4hDozDfJtsckXNGVgd+VxNj9YtE23sVuxpwxldU1Ym19hODceFGuTbyxielpNeP/UQqLFEi4arcmvwCuznGUsimZC5pd6CMHHS4KLj3CCv319wp/DNVYyRO2rlSAdjcObHcuKrHdXZIH0WzWVh9jHKKU49NMNWCMRPvwB2HZ6KpT1owMvcbvFs1n45gJhSziHt7B3DeIPUHyyZDtqm+zDAW4qrNL7vdX9+GtZsacUGwur9OObC6tw1svTXMWf1sPMCdqygEkp4IQykxAejzHru+2hv7N5B4J3gwBl9rm3ztiJtaUNIX2txkVoLbf49XlFExT9gYi0kTJhz9xXL50cqggx9DGH92px4Fj11dYKLCsM9nsn5lIGza+kYNkSN4uH2hNMWlr91Ha3ScSckjhaxDraCb+dUjyY8Jn9jnHQtlde0JLCOjy8MF/qvxGteVeiUBneXKBvXQWonn0t3I/a0OLDnyct1j/7AzRsqB69UbdhqLMQhwz9nsHnI4SlHdWPtEF7tutYPptBp6axRZiUrcv4fvVu13XaWVqLzYVV2FpcjakbgkG+t5UYQ0v9vN4YAPyDRVvxVYzjCQe3ggk+XRqaJS1e2JktiBOhVUg0JyG+rCbmOTYOFjwupVP4FqpoNmKn+Q6v+Q0uJK08q0XcmD3omt9IhV/JgWW7S/HJ0u14c16UmQ0RXHDlFht/H252sGqvtTmNuY9yMyzR7CHoCBx5246xr5wl5jB9XGgIUIoN5Y2Yk1+L/yzOD1n8xFP45Y6S/I6nfrMlogWmE+HXjvJGH95cGxqCT4Y499lpWEUtc5NMNW1CPOOFVSwc2rrSBkzfXY1f91SH9Mv82hZkas6O4q5Vgy+AhySJHmS4edNO5nyximkWWnEvAbqkeVAZRvD2U4qGVr9ttBDA/jewI/lE3xiHOuso/LIhD79syMN715+A9BQxtAsNsadbnVeOLUXB7Y4XZq7Hr5sL9PBDMiLZ3osWPRSP6a6UGr8TBwWZGPDm3M2oaWrFg2ePDzmWV1mHQd2ynVVI0vn9AYp9VfUY3D0bc7cW4ompa/He9SdgcPfQMi9/ZxZ6ZjMnAbv++ua8LZbHmlr9KKxuwAE9O+vfrd5bjge/X24479xxgwAAd32xGFY0tvrwxXIm+F4xYZh1hVyiC78mQYnbC67Lr0C/rpno1TkjZvcMhxOt7px8a/tJjpUQOjnCwP92iK/PLoZxgNq3J0rdbUGai1q0oxgjenVB7y6dkOIxTmhBzW8YASnMwCFWjy8gG1v82F1eG9WCNqiZFRfMRMgYZnMtNXqwcxtGY8IFrXw3dTLberbRqLrIFHKPP0ZBfSs+3MicQYd0TkdtG4aL5P2J16Wo3t1CUS9H0j+cCL/8531oQZ7jJBziz+dU8Kp2sIAQQyIWN/jQ6AsYtNW9OhlFo2un7sBfj+gLwDi+LSiotY3SIOKmbzkdQi75aSvOHNwVaRYp3tK9HjhZlwQCwEwHKaad2keHlK80v4nHH6B47JfV2FxUFXVZVQ0txtBGDn7gX7XQRVyjUV7fpIfk4vBSgu2MxN1gXPdG1qNYB/8LhKqDARi3JDnfr9ljmf3t1o8XAAD2lNehujHM4Cd53M+X7cBtnyzAnvI6LNzBVqjbS6w7bFld5LFvAeDp6etwx2cLDZr6OVvts0RZIdPW8d2DaAhGezB9r7WX+79dhjs+WyC9dm9FXUjbiwWRaJNkyDK8xQsPYc41jyzOt72vP4zml4K6En7NYcse+Xk17vqSOZuZzR6CNr/hNL/mBay10Eh0+32K2z9diDs+W6gfW7yzBJsKw5tucLiS1jxU8fvbOdpZaX59Eoc38VSexfGsl6dhusSxLcTGNkFu5+J28S9atr8u6d429YLnQmu0gogsFOJvDgQnCpbSW5aO2Qrx/UQqeDnBLuMkwHwceH8Ud73uM0U66J5hrU+Mde0DlNnevrCqKMTRj5PmDY1BLSPcuMaJJCyc85Palg4n/FbUN2P+9mI8FkH0ATMvzdpoGEhE79z65la8MmtjMCuTBm+ifAK7+r05unBS39yKJ6euEezvgoJNJP2+qdVvG2FixZ4ynPXyNJTWNoU4sfkF9bMe3qTFh1+Ebf5It0D+8OkC/PHzoDf5vG1FOOvlaaistxdW+URcWifWN/b2RtxOcN0+ZuNoiCsaoQmKzETjbhtNsVN4qeZoD2JbrBME3Kenr8NfvloCgGW9uv/b2OejESeHYV3Tbc60x04DG2ta/BRnf5eLTzaX406Jcwnn3fWlmGUz0QeouyZSK9GSVze2orHVF7JNyn/jZpehzsxvUWwqdtur/5m8Cvd9tRSvz7GODyqiC6fCHX/bUqA7ltq9l5AkBFpZfoPmFyHlf79mD6ZsYELvewu3WtbJ6j5txfqy0IguWyub2tQL3qz5jZQftldi8k7joqjAoRb5vB+2Ym+tM60vYFw0hBN+Myy0n04w2+2a79QpxaO3JXMYO5GuabGJLNTowHRD9ClItXj2Rl8AVQ5sjf2UOponZbtxTppTMoY663BmD8GoBu6vNa+N8ivrDZozX4Di+xU7Ud/sgy8QwM/r87C3wrj9leL1oNUfwOIdJTjtoP4AgJJaJmz9tHYv5mwtwp5ydg3XXBKT5ndjQSWW7ynDTcceaFvfe75cjD0VdZYmFlM0m9QtRVUhGYjESccy+0sgcs2c6IjysxbRYXd5HbplacKSpC+LmqNgaDbrH9LrIfAHqOsJ5pqJc3D/meP0KohXy0LKfLFsB04Z1c+2TNlCwW0T9AcC2F1eh+G9ugTLsAh19vzMDfjXeYcavluXXxGild9RGt78wC1c8zsoOw056ZFPBpGGBoqESVuDzlx2XvZFDa2YuNE6SgiFuzi0Mg98ALj4jZkh3zm1+Q2pE6UAIdItf95uZGmQOT+udRZxJSAsmDliuXavxVw33l14v3ln/hYs0soSu7PY/62cdaR1TAJ4XNq2gi8mq5p9tgJcOHjKYLdEoiwRLwl3fU5GSsSmHGY7YXMzyfB69LrYRarJkmQAjIQvcsOHm+TOvZ0sEq8AzsfQAAXunxfedlnmHOis/OTpd5wOJ/xy2SWiFXeYS/wBioma9uHSQ4cACBWyU70ErX7gqenrDBo52+JNmt+/fM20dTceM8J2ot2jCd4+fyAkbFLILfiiQHgWVicKKxnXbjCS2T+bqWlqgZd4gpEmHIuDNCTXuIwUTfiNZKGzfl9lMJ6xcL3skT5YvA2zcu0nBDvnPKdMXLgV36zabbBztspQtnhnCdbkGwfQWblyc5Scnn3Cevu7gU+y6SkkKu1tW5o9xIovt5RbamFk1LUG4M3uDgKgW6a9ltyp5tdMgAJWS5BY+tKG91UgWLC9CL07d8LIPl0NR0I1tJqWkqfdXbVbuE/wXHHRZ05+YD4XAEpqG0OSd/To1RdV4cywkowTB2RjUOd0RzG5OXy38V+L9uHFVfaOTfEgkqHAjeY3J80bufBrGmvKTWYQGSlB4dfOqTZWwq8bvCR653OnCxPZWU5MiZJP9O2AZg/mAPRzthaGBEH/Yc0efOcwCoCVza/Vjy06sbwmbCeW1jaFRAcQkQmGTldhTiZLK81vZUOLwe5OxE7zK5o1WHH527NwzcTZ+menQipTZDnR/Hq086PrelaTrUhTmHccqYmIyPp9bJtrwfbgxMVLlU38//jBWSi3V35cjYF3fYyBd30cdR2BoNCa5iFRaW/b0uwhVryyphjPryxydc3Auz7GwQ9Mwszlu23PcxrqzNwnWv0BlNQ0CskoQsuMBUGzBzkeAvz3lzW4+8tQcx8rO+EADd25ET+J1Zdpfs3dTjYufT03N6btvy3wEhKS4jYcYvSRijA2rsmCuAMZTvjNjsLkIFx4tHRvMIVyWaP1u+scI7MHN9S1BjDVwunOaRtxYhdshQMLjYTZ2tvR4TS/QecP9v+TU9cCAM4ZO1A/5425LOxPv66ZOHZYb9vyDOYBDn5gKw3sv35aGRKTkkMsym72+ZFms+XBafL5kZVu1HaI8XoBYwpl8X8AeMAiuL2dQLezTL6dbp7IGlv9goOQ/fsT+7GebtGmDjKzBadYba1ayQrhxhhxAdHqDyA1jCZeBrc9/nDxNvTp0klPOsLq5W4mFH+HWO9I6Zpfrwf1vsgn2fao+Y2UplZ/2IxeBVqc3MZW4zttaPFhyoY8XHbYUKkwe8mbzIRi/IBuAEx9MALZd0tRFfwBirH9uxmcQcUUqTLs2uiuslp9R+uta44X4uKGJhEIWCxGzcJvXXMrcotDhYIApZixaR9OP6g/Ur0eV5nUkoUUD5EuePc3RMGqrNFeq5sdhdZVlgDHUHaaM+fERGh+7eiU4nEUctFK9j8wJx3bTAlCDuiSjl010TmTJwPJ9Uu1AXmVofm/rZi0YmdYWxVxxbRR8Iy2mgCscp/zpBIyKAVqhG1pLjg53f5sljT+P36+ELXNwTJ5jFjd4U3o6cW1cqE83Fb+8t2l+HWzMSNdq+Sa0jrrdJg/rt0bEtqJwpnNbzBTXvCclXvKbOvMYeVqAjalKKtrst0NMGfEMiM+t9tta474HkTBF3CmwRPflPje8qtia3vInSI6p3lR2RS5bWGsoka0BzqleuGnzp630WSvOXHhVrwzPxeLdmg2sRbXNWjXRZsZ6s+TluAvXy+FP0Bx8ZtB2+RwY6XdrcR+uamwUi/LH6Ah/aVASHwjCtTm0HCTLGJqz91aiBdmbtDTscui1iQ7TPPr7Me77/C+ca5N2/D3BfZpqqPRupbaaHMBJgQ6yeaa5SC7ZVtycM9MR+dZadUzJMq1Hp3c60yT0ea3Qwm/36/ZjQe+YzFanfwWmwqrcM4r0zFDSyksu0QUEsV4vlaEs72VUdvcius/mKt/5sJvk0OnBZmwVW2xitbNHhxMxOFCLj3840o8O2O94bsLXv815Ly9FaELEjH//KzcQpz18jQs5xMkDU3HLEPMqsT50mGSiZmCY1iAUvxn8iq8NW+L6/BpvH2I7US2bf3Iz6vCltVsIwzuKAsfakhEfCfFYQR3p9x+cC8AQaH1wJyMqIL22yXS2N8Y2aerpW29mQZTkpzJmsOo/r1Fl9B3Qgx9JnLtYY3JTjaYhEJ+vpj1zcynQvIcjyfo4LtoZwl2lxmdhqdv2qcvJkXh3az5tVoQ1mpb/lzhYGXWlcykeAg8DqeSTlFEQWhP2Dl+RUu4+N5tUQe3TOiT5Xhxa7WLK3ueSBbMSSj7dizh9825wYQGboKdvzp7E8rrmqQ/oBjNgQuD6TYdwKy10REaVLiaudX8hrNHFZEJa1a0asJJNFEfRAzJNCwSa/BjeqizADPh+OePK1HTZJ6MQzW/VplwZPCfJBBgts+A+47PQ82Jmt8Wf+jvwbV2dtgtNqZusNeK3PbJfGwWhA9x0bBn9Tw0bF+Khu3uwp59cs4wDOmSpn8ekWNMpCEeiwQeTD4riSaUeNCwfSn2rJ6LuTN/dna+RYZI3g+txja5GZC7WWnV3qCGtqLBuBDku2BW9+dCejg8hOiCRkV9M/7vm9B2yeOEi4tkc6IXq+gP3NeCUpZ6+bsfvouo/SeSFA+Q4lDz62bMSxb+ON7e3FBGehyF/MomHx5eaD/GApEJhvGCwLmToZXwKzPjiOQRleY3ibCKKymj2efH8zPlubFfnxNMC8oHZDHrmxlz4HoZu8vrbI+71vyGOU+0/eVaECfCL7fJXGmTttQNbkzvxDi/363ejWW7S0MEwKD9cvC7SDTvAUojFvD/8OkC1Da1GrRLVg5LZ708zdaUpFUiNDtlb0W9oV2Jg9GPz96N0m8fQ+m3j7kqs1OKx7D1ap58otWC8JBCo3t0Cjm2+caDoyo7mSj99jEsfutveODOKxyd32ihTecOsOHmGdFkxq2569+/X6H/Xdlgtdh0V6YZr4eEnSwrtJjgBs2vSRg0m0GYKappwL1fLcEHj/4hovafSLyEOBa0nMw5ycbgCBbOaU5V4REQjyxubYFTe3Yrs4csiSzj1r8EUNEekgqzsBsucoJdJjEOF36Zh7L8nE6p0fsY8sGsvL4ZAUpx0Ru/4smpawBAC+1lvHmeKdGF2e72J0Ej40bzy21QY+Uwwn+T6sYWg5bezHer9+Bbzf42QClytBBRFaYkGbowAIrFO0vQ0OJzFAuUw/u4PxB0uonEK7awusEQeUFm98x5ZsY6y5irbuO72hELZX2KxzgBZwv2bhcOy3EV8ssMQdDswVzMgOxUpHk9OKG/w1TZ+xkNrXLN72tzNqGivtnRRHPWy9NYPPIonAqbTPVw26YqG+QmRB4SPqMlv5ZIHN4aW3zwB2hYoS/SRfuJAxLf7tw4vCWZD5YjIolCYpXity2Jp/Y5EpzOV/P2yR3UM2Ok+W3LZC5OaYfdIjZQsJU/xzyQm3FiF8Y9n+2iIFhtWbqBO1c9PmUNJq/bi6ZWP+ZsZeGVzn11Ou7/hkVn4OYXK/eWoaCqQRcOl9gEtG/1B7CnvM4y8oT5XCB2Zg/bSmoQoFRP8sExC/Or84KTViBA0VmLZFFjEat2Y0EV/jN5FV6ZtRGF1dYJDMzwLVVR8xuJoL9kl/F9cyF2aI/QSXTO1iL8Z7LR/tcfCIDSyOIVW+HG7MeKVA8xDISit/XTJwyyTLnpBCdzyG0Hu98a3R+wa4PPzFhneczcSy99a6bt+eEw26AHzR6c8YtFaEem+bW/lvchsYmlaLbCF785Ey/P2qiHOowlZwzugg/PHh7zct2SmeLZrzW/kciQySB4XjCsG/44vjdOGtAZADBWsmvVlgzpEnmGTcDo8Jap/T2qW4bV6ZYkY0CVjiv8UoobPpinf7bTxvHj4QSGVl1rGvQL3VBQaTjHqamCU2TRCzYUVKKuuVWfnBbtKMFNH83DVe/N1kIHWT+rP0Dxh08XWJp5iCzU7FS/WO7MiSwcny3bgQ8XbQtZ9dv9Nn5BMLUKNbelqAoAc5yLJKNZgFJdi1wdQTB8HkmDc+9XS1BW1+TIDqqqoQXnvjoDP6zdY5shyy2xiLvINE/BSomezh4PQWoUwodov2nelOEid7zm9B4ZznZnBneOzqY5HjS0+NBqYeMv+80bonBIrGs2LjZlNvZ2WPkseEl4s4enp6/D8t2lJs2vRx93p23MR0oSCEPxIifd61g72h6F30iGp2Swbc5O9eD+Cf0SEu9Xxn+PHaD/ffqgLjZnyskUhF++uB3dvRNWXTsWa64b57icJFT8dlzh16y1aA2jvbTztOf4/OG1g+YYndFijt/L4fGLzVBJ3EyRcIsAkbK6JrT6A9jmwCTEKfO3F4UIeXaOXgFK8dGS7QCA2VvlWdbChSGzgm8v+wNBAdtJRA8n/LBmjyMBtLyeaeCnhXFoc0ssVuLmSVV0jvAS63zzThC3dK36U7zinC67Zqyj8147bQh23HKI6/IjmYScsqWo2jI0Yay3Hs27WME4v86utzLjEaM9WF7rD+DhH1eGJLkQr3Nj4tTeyElP0fuflwBfnT/C8lyr8JrJTCQJbpLB7IH7OfB26LZGV43qHtP6ZKZ6dQH2ylHd8dIpg91dr1174oDO+pyR4iHomp6CzmleDMp2pgBQDm9JjBOhb2cYrWGNFj4sQK01a1aCZ6Td1koAWGERzzZA7ePzut3Wj/WEWl7fHOK1PWuLPC0vAHy2dEdYbWykNslcS7+5qCrm2zYEzgRQnsSk2ReIaSpat4PRX48IjRWaarL5Fe3DCGAwe+jqUhMizmM+i7q6netSYjw3Eoe/yGG9jbE23zpjaEK2aGPdhs3CKy/f6QLaygTMad8AQtMbG5xb42D2EO5Xm37ZKGlfiTVd071637tpTC8c2ss6nqsTR7APzjrA1f2fP2kwPj5nmOG7s4Z0dXz9+Qfk2B6PJMFNMpg9cDMBXnu3644ucdQYewhBhkvteGaqB9MvG4U3Tx+qa37FIvLq7Ofe/llMOZeEsq8Sfjl5FfWotbAZ5cy0EcIAY+iftoodKQqKF7w2I+z54TS/c7e5S80aD6HQjFW2OKBtvEjFiB7RcPWRwmRBnDkjeLQ30uIPxNbsweVo1FMS2DzFZPMrbvURQgyaX/6sn54b3l4yhQSFmtMGddEXL30yWR14qeG8jrtnGCcSN5rAx44bGPYcp6WNM9n9eQiJaVphp8Ra+2I2W1i2qxSF1Q0GB1o79lXJ7e8DNNRp1wrxLZo1xpFEdgl7vzC/24icjKjNDAZ3TsP7YYTRzmleXfERALUdG5zswJw00N1uxIHd0jFQ0Pod2ScLb5w2BEuuGoNLR3TTv7dyDrz3sD625UeSFt1ttAdzxJj7wtTJCVwAj7SrxbKHZphW+5GsDYZ0TseInAx0SvHoc72bnYR/HcPMLpTmN4l55OfVuOOzBTErr61SZoqDfbikEwC3kY1d3WLdqAkhSZkHPBYc1DdH/5vA+jkJgP/+shr5lfW60BhNmDMZbtuATGOQ7iX6pPvV+SOQbjpHHCS5PbwTJ7h0r0cfqMf16KSnOL1udE/jiWHa3uG9swyfncRF5Wcc3jt8ZqRo5NdEKKlivSA3a35/2ZAXElnGjiqLHZsAdS48vPjbRv3vFJOjXLSTm0yD6+RnczskmmPaHt03W6rJFb/LSPEEo9FQ+3pF43hqRarHE9LfCSHolZmK3sJC2cp8IdwCocVmfBIXk+I7cWv2YLYR/sPBvfHGaUMM343ICXUYe+qEgVh2tdw0SgzBCYTuDp06qLOrOkbDMycaTRw8hLgas+4+tI+0vm4Wd8lscRMT4ZcQcg4hJJcQsp0Q8vdYlJkIyuqaY7aN73aiaStFEKWxnQTzXUx2TiBonxmXnNApVdSMWi8cKIAF24vx6uxNeESL/FDd2Bo2HJ8b3Ia4kq32s1I9ghY29Bpx0uW7DU62JtO8RNeAeD1EF9TNk224J6g3pfV2kpaTV/mALuk4MMfeqzmaPpsAxW9MQ+UBcoc1p+Pnqr1llos/N5pfEbPN71OmFOBuyUmP/Rb0/CtGh3xn7lseIg/19Zmwa5LuDYY6CwSorUY6Gtt7KzJTPAZhU/y1zhmao/9tJcOGE6DMPjaiQP3E8cFdmYN7BgVhN8LvhD5ZId+xsclYhsz59bIR3Q1jiVgHDn9s82O+c8YBePcMdyYmbll69RisvHYsememGuridswZ16OTtF3ZKRH6ZqbitnG99M8eGBcDyUTUwi8hxAvgdQDnAhgD4GpCyJhoy00UsfqR7EwLYonbuLOU0pjmsr/7y8UxKwtgHbSttOZtTYYp73u4trY6rxz5FlvD0fKvn1aGP0lAZjKQ5vXog6NsIBEnXR4D2MlEnO41brHxNs4FZz72hmsmda1+7LjlEGy7eTzWXDfOIMz87wS5WQMXOtJTPJh22Sj9+/OGhtozcq2OONg7xRNTC25nONkZcoNM+HWa/ntzUbVliMSAJFa5EzyExHTbOB7RA6wEtBXXjMWVI5mzE4vhG3qOGHYqw+sB/2hlE6/fMw7qt75ZqQahXfy9xvfKxHtnMgHPan7yEoK7D7U2MxjWNahx7ZzmweKrx+r9X1wYiMKZG1Mi2ZmEEPx/e+cdJ0lR9/9P9YTNOd3mHG7D7d3ecjlyx+UIxx0cHAdH9AdIeEBBhAMxIQIPUcSHIIoE9RF4FBVQDKgIgiAIEk4RyVlAwt3t1u+P7uqurqlOM7Mzszv1fr32tTM9Haqrq6u+9a1vWNbsbf7Bbvtr8xtx18Yu3L6uC5u7yu2TAVPiFI8lY+6YV5YTRmlOrNDO+wft21jsGYbNqTrdJi6/3NSDM2fUxZwjE4f0ZLzdMwA8Ryn9O6V0N4BbAKxPwnnTgpMdWlCSrWVJ1nVGqTy9bqZACEnZxCHViAlO0jkb5oXqaE27+ecEm+0varAvg7nZ3zIbvKhGsNnwYs7zIVDoml/DuYIATGYTtcZOtcfsJZknskYIiqL20FA9ZfKO36lbv2RRc8w2tu8qD+cdGfz4UdPc7Vn/ySD5mt/Y89371Eu+jh0dpY6pV92SBHkRz8odq/uhPssGNKIRM1Yrj5/MY27vtcxJkgIoyw2j2JichXwksMgJEW6J3b08yRLiT+fMQMIasYXBEovABG5nzS/Q4lKXGzvK8EXD7t4Kbcj+y4/xkikvWdiE8+fU27aJJieEENy+rhNH9FWa30XYtgM6y9Ft9CNfmdeIp7ZPMfdxkH0BuAuPXs33s/vUuu8A5/rRiLw8h/VWxvgluOFW/hwhqyfbMxOTXCSebgyoB8BHK38RwExxJ0LIMQCOAYCmpmDhNpJFR1UxnnvDPSzX8TcnR5P54PNvBNrfr8ZExClWphNfvOtRvPdx8Fi1fskJh7BnZDRuwU6bwJrfKNcx6LbNaSwMR+3hl3ruo3nM4GXdITtmZm0BPjNciwM6ytEkCbreVBTFC+9bbTInRMxBIKQRU7MVY/bg0MYWNhTjxuVtGBTsdvk+u9hpSVu4kauXtOB3L78v7fDZuMiPj9Oq8vHnN7wn0LwgfvwXb8S1TwTrL+Ih2Xbjsr7nr6+86+tYSmnSzR4ojU/DxNr/gzsG0X7dYwCAJ7cPSDWJpw7pAuDnZtShJj+Mk34V69znVgSZHGqa+BjXCxPvJercsGUX71VXfjWNpTkhRDWC1z+SR+HY1FWOCx+2HKJDGsFtqzuw+SfPxdw0u6aTIiNECDa0l6GhMIqD7toV8zshBP2GOQExj9H/2zS/3DFemt917WV44JUPjPPr204ZmoRH3/jQvBYADFTmo7koB99/5m2cOLXGPCYIls0vd0/G/3gcIrvLcvH0Ox9jsNKPL4L8/E5ToP2airFfUzG2/cwer9+plEHKP9E1v7KaiLlVSuk1lNJhSulwVVXwZcJkUChZChCRdejJjhdZWRg8Q4oTQbU5f/7XW46JHopy5TGDg5B4VZGkZYzLNPh2RBBfquRUcPr0SSgXbN1Y0cUSO20HgLrCKC5f3IzLF7dAIwSdRmagnbPqcUBnmeP1oyENHxj2umFi2fyKg7hb9c2tL7KlW9bLah1fm++vre/XXIJzZ+saqGMG7P0Wu3e+Ex2qKZCaSIjw78kz73hnU0wGYx3qLAgjlGKEjmJlf6z5ydfveSKQ4xxjFDRpGiYnQYppUY/sr8KaNuc27IRMoyvGhNWIP80vE3JEDbqY9tuv2cODB/fh/i29OEOiXbxsUTMq8/R3hhfAeivy0FOWi8/NrLPtz0winPq4sKaXf59JhbjUIfZsSND0sirRCHDxwiZ8fmadTfhg3cPMSbH2vAyxJkIawXdXtuOMfezlL84J4bFtA5hVG18qa/au8YIo+xg05OKa1lKzPhMZXzWNoN5YDRuqzjfbXYiQQBEc/JR/Q3sZustyufco88a6ZAi/LwJo5L43AHCPCZYm4tVGLu2p894pABUFiaUc5AmSlMKLz6+amvA59LYeW89+Jh6AIRTGMUqvnRJsNSGZz8AvovCbiU4AgB72qFVYkmwwspnNri3ERQuasNMIYcOngJaxqrU0JtvRYb2VNk9ksd/lB2td88u2690V60/58/rpkPnrOGnD3E7zWWGAZPfO33lUI7hkUTMe3xab/WjfxmIz1Btfllkug3UmE3TVabDBCuA/akSdiYY0VBYm51389TOv4spfJycsYSK4vdZuMgbrH8JC/Ozb13XiTEEg5SOiiO/eZ4R9ndr6ebPtJgAhjSCkEWztqYjZl62+3LepB99daYVszAtr+MnG7pjIKsy238m0hdceVjtMRFmxTaGXy+y4vr0MR/RV2TTkGiF49NB+3LC8DenGyeENkGtO3cwZjhqosiYA8ZTFeHYagJ7yPNx7QA+OnVLNrTgEU/CJcfhlXLSwCXdt7DbLO1E1vw8B6CSEtBJCogAOAnBnEs6bdOJ9ABHJWpXT8/ej1dWItV9vbWl8hTIIOgC5UeSQLU7kiDmd2DA11gZSR14xfrXK7360Gy//O7jddXVRMG36ij65w9P6wbEzybFpk0hyUgw3l9s1E34nGW7I2nZbSS5+s3kyjh6owoaOMhzWy2zi9N8TuRNRy9ZmCy9EHTW/g1X5prOZn6U4tsdxU6o947UG4S1umTgaIro9ZCTWrGJefSFmG5ok/vrbeitj9h1rRNu8eAja95zNTa5HqS60hTTiO1mIH+57Wp7lsaIgByft6y9zX6K4mj24tDv2RDRibx8Dlfk4asBum8pHexAV8KIzpZNWz0m7LdvKQhU2FedI27YIS23unJ3R+rxPTQH25bIe/mhtp1A+QQPs0l6KoiFXG+dUyWA7Z9VjSWMxLpzfZMYbl5k9/HJTDx7a2uc5YTLbTQL9FqvP1pIce3IYjQQSBIP465njw0QUfimlewGcAODnAJ4CcBul9K/uR6WevSOjeOLld+I6Nicc+7I7ZQ/ys2RPiDWzTzTYPctCJqM8oHYzJ+KvOWwZbkNbpTxeodPdBDH1uP73z/rel5EsLeq+3XVjNkjyA993/7grKd731xw61/b90JnOaU6deP/Rn5l/gPMzrC+MxrRXc1ErgeoXx+bPz6jHtsm69mnPKDWXTpnwy+++n5FVyo8wy8rOlkW/PLcBd3MRHYLCLjmFizW63GeWKz5c0I0/uNlW/8nA690vzU9c2+on5TtPcZ61mkANza9GiC9NEgBUJWAuRgiweqBR+hur+2/dehNuW92BE7koBNfu1xq4jbi9CrJmKoai8jMmRDSCuXVFKI6GcJSDOQ6DFwZlZgIi+ZEQLlnYhOMHLYE7N2CEAj9mD2aZCMH/467F3idWbN4cRP8vv2aQLih50y05rSU5uGa/VjQURfF/67tt12wp5iNZhFCeG8YMo0+aL3Gy1MCtBFCK29d14rJFzTFJOrwQ6830q3BIuuM0yXCawP1wTezYY2l+M0/6TYobKKX0LkppF6W0nVL6pWScM9nc+9RrcR8blWhJwg6dgZ9IBXqw6cRteADgIxfhN+g0Vybky9AIcSy3U79dagx8Gx01xomRiBKVf/FzIyHHweec1VPjvwjky0Vi+LOg8EJfbUkeFnW5ewPLVhre/vkV5h87p1/NaDI0v/yVljYVozjHyl41Mmq9UzLtLgtC31PmLRiJr+yW7gq0C7F8g7yObN/y3DB27RjErh2D6C735zV9LZfB68Rzz7TVfzI4dr67wJZoFjIA2J3AqtPIqC78hjTnvkTkW9vmOf5WmucdhQHQ3xERVvefOucMTK8pwMlDVlSDRY3FMW3EE5eBXm7za//vpzoIIajIC+PPh/ZjiuAEJb67/LP2a9u5rr3MZo4QNKIJe9ec5vdiHysrF6sr3taX/w8EF2LTIYJZNsv6h9ywZkbKYL9Nqy7Ak9sHME8i/BLCJc+AvhKwuq00cBQPp3FNX2nQP3eU5sTYjLudZxo38Z9aHWu+JSb9yCRUhjcfyJ5bKA7NL+uECOGN+RMbhD7aLffMBWCGi/JLrg/h96yVgwCcNW2EkJj6OmxWB4pz9cGpoWxs7BuDmhDY7DT5+JmRkNQjG0jcIdBt4Ksr8fbi9WLU0KS54WeCE0QushQS8XdufFtidcRs0PZSKySWTGArzQnjro1duHiht7kKu467Zs7/zfutp+lGQH0+vFqHT4Fqdls1rjp4Dma2BHMSri91f8/82PhdtmUWLt0yy/H3oJpfEd3sQfNt9pBsx+Oxwt3sAWjm7OknFUSw2YjvK4bHOn9OPe7a0GU7/tbVHfjxevs2EbGW+EkfL2R6Odxu7CjDmtZS/PHg3sDjVMhD8ysii/9tjpHGd9lqqd+2c6JLTOGxxtXOm7sXMZpNlZFIY4RSz2g7gG7O5YZYxXyUEXb+qEZscooM/n5uk2h7eXrKc3H9slb01gZLn50Kskb4FbW3QWwjZQO7k+ZkZJQ6vo4sziufZtDvkp8TH+1xFn41QnD5ltkxdqFO5ERCMc59B05vtX1nwisTEupK8jGvw+pY+LvJCYdw6tJ+HDqzw8zaFkTjdMtRi33vm8jMkrd/jIY1RwHIydTFL7JnzewmkxFpY4R6CwiyVQwRjeihnCaXJy8qiRuypVg2SO/lzR5Ypyy8Yd1leYGytyXL+cLvwLu2rQz3b5mMmYLnuCwzlEhFQQ46qosxrSnWCUnGqUv7cfKSPnTVlGDpZGdHXT9Cfml+FJO5lNwivOa3Z5I/cw+GHs7MPvB64SaAOaVKtkid4Lyps9zxN0IIfrrR0sr/bkuvGf5PdJLa2lMZs5IwXFOAyR4xWWMjsnCaX04C2uPxIhREQrh0sRXlIQjmyo3Pfjkq6VvFKA/SNuvzsTJtfjyT9F9s6sH5c+qxrq0U03ykPRdh/QSxbfOGad5HqHXvbvV5+nAtdu0YdC6HUH+8uQ2zEx+hwI4+faLt1D/xArvXpKg0J4wFDcUozfe3MpNKskb4FTVeX94w7PtYmX7DSRDaO0odZ0zMppZSS0Pnd0bt5Izm1n9RCnRPKsGibu/A2IBuG9ZVYx/Ejp7XjbtPWmHFWjR6JNaH1pbk4ZzV08z9+du5Yft807GMOT44acxlSzhBnHISEX6jIatt5IadzR5kjo9ulOZFsWXYmjxohOCiTTNs+7CoE6IN9dHz/NsZXr1Vt/tlDkRu+NH8EhAMVuXjxxu6saTRfcbuR5vqBV9k1uFby6bUin/qcm9eoaH466RqBe6RQ/rw0Fbdfry2ILbz/86K9hjN3s1HLZKey6/Wc/KkUqzq121bq4uchSS+vpb31kv3KYi6Cz28iddwczCnvTse0+Pjapp/Exu+DvYJeD3WQuN1rvv9Qb144CB/iUvrCt0HelHDxzBjwya4Gugm4PGa37HMw1RmOHmdNG2Sx546bl09e2ahJL6/Qeq4pTgHW3sqccmiZvxgTWfga5lVzl1StPOWwd+v0737SU7hFHmCN7PhFQNz64uwa8eg46RnnCzAeJI1wq+o8fIbTqu9qki6pO5k8+t2XiZ4fLxnJPAykl8BlsdttiVePyccMmw95fuHjQ47ZC6J2P8zRin/YvMdrbvmd+eaaTHbQpqGPJ82sUHNHuwaal7zmzzh91MLJ2NSsaUpCGkEA/V2rdC6KU34yoZhLOi0DxJ+NLQM1hb9mD24mckw+Ed01ZIWPHGYs2NFMgRKVuYtXeU4fVhv56yd7KXA/67txMlDk1zFFj8dsqXdcC5skLfSa8JVkhOOiZfMUxQNxWj2KgoEG2RmBhKHQOT3nXByRs2P+l8dc5rUAkBJXgSXbtbNJ05ZYncmDQVweOPbdkd1apdRa/IjqPIZGzpegtj8up7H5Te78Etx/pwGXOLDZCgoOSENu3YMYkt3Bb42X+5oaC+Xc/sxNb+SsIp8XfnR6qbT5tcv1yxtwU0r2832bjd7sN/BLav9Ozg79iEEqCnQ2/b+LvHXPc8zzsga4VdsgH6XY76xda50kBOFjAsP2MfzXEyA5LXDfjWWTeW6uYEoJLkhEygZoqPVvj260OHUrJngFyLMUF++5yecA55G7B0toAuAqySB7fdpqcKkYrsgwOJO+iFowgi7zS8XL9blmk4THicWd9fa6kBWZyGNYHpzZYwAEMS+ka1COGl++U1+IkzwZwhrBHkugjjbNxElEjvH5q5y5BrXajaWgusKIuityLPZ7Mmanp/JpJPZQzVnMhHkCadiIBVXXDz353ZbP7UJvbWlOGpurI0ov5/TmYO0QTdHquW9DZhsOFqu7LcLQkHDLDHcotyMdxKVLdy6wgs4QXTPKMXWngqsa/cWeBLhgM5ybOlyNgUB5O1nVJgMmO8vt0+8dZVK8Y31Tfw1WXQL2SrAkqYSzKottO53lHKCv31ft75ZxK2uSnPC+NvhU3B0v7dvgdL8jjPERhMkkYIf4bfQR4xcKy7jqHm8X5lt7ZQmfGHtEJb4SLix/7RmfGPrHNQaTlSytvqhoAH00hJFRM2vsV08Ny9c8S9JS4Vu71hdnIeTl/RLNTeiYBZE+E3E7IG3t+XD0HXXlGA+Z8/Mm7o4leq/N9sze3sVnwm94mw6iH2xqfmlcs0vq8OV/Q2+4hgHWZU4frAGIQL0JmAfLLvcurZS3Li8zXQG8sLPvMQy0bC3lT8c3Ic71gVfzkxF4HZWN26aVR4+xFlFQS7+e/MsdFZbpkymMwvXgvOTEBvarXxu1cRHvuGTYADAGcunOB4n9l/eZP6ILUuJG9d5XGp8Tl0RPm1MJP1EJko258yqw5OSEF2yRBwFhpng3Loi2/cMDBzgiqW5tvjUYA127Rh0nTQyU8BRJMdfIfYNtZsCRXyaIInjw+rWUpyQRofCeMka4VcUdoMsk8teNjH9Ji/8DjfLZ09M0Nk7YgkpfoU2jRDMaqv2Nevaf1oL2qss4bK+1NtI3+oIrQvw9qpM+DWFXgezB1uZucIeMqMdl26eZTnQcLd9xUGzAQB7BCM0jRDfg37QTqHJcNzbp7kSQ412ZyKbN7HNWYQTfo3N22fbhabeWrsWxaszMbUCwm6ieciZKyxHBtE5LsJpfvnDvrpxGBunNkMztPUHDrUiN5K4oMMzt74IzxwxiJIEBChZDRFCMLe+yFZ/ZYYJgSyWrr9OW/8vayvMTjPTVvRMswcfL/7dJ62QTsL5e2JOtzYN8WAzjp3fY4s48t0dC83PX9kwjBKPUGLiqsiPj1+GI4x3w205Wg91Jr9HN9OfZCYpyRRie+A4z8NVt8xp1TQpSqHwyx5XRCNSbafMHK4sN4x7D+jBF+boNunfXNqKk4cmoYWLljGvTh5v3pE0CM7imOmXC+c34vDeSkyvLjBXxBJBfL/ibW/iPOWyxc04Zcj/inSmkDXCLy9kFuVEYhy7eCYbnsvM3tSPgJrHpVr9/KqpuPaw2JiUvOY3qNkDw88LJNqmzmmvQZmHt+VeQeu6ZqARR861nK7YLJSlU/YjhPPapZCmmUufIhVGetPdI7FLmTL7os8si9UcBLX5XdxdiysPno0vbRiOefuDRHvw0qR62TM6OT7yQn9lYa5tAlMg2GKGWCrRUXvZh5oq8amFkzk7bX9CVFATklRRnhvGQ1v7cOr0+DpaJ7s5/rcgBA0lmAjicztuQQ/mtevalpywhuMW9Dgey7etOqMd8Q6mkZCGA4ZazLbaXFFoc5ab3lyJJT3uPgcywdVPWwtpXOQbIp7D2d5/rxBS0jtUYHra9LSqfGzs8GdWYC7zJyjYs5rpq8jDjzfEOs5a9vSpqxPLhEF+b04a0NaSHLOt1hdGceLUGlv9zKsvwulGf9BY5D9xy3iYOtUVRnH2rHqENILzZtfjyL4qLGgIKOxziPcc7+NPNDxrppA1wi970BdtmoEfHrfENbTUgdNbce6aabj6EMuL3gtbxIBICI1lseHFQtyMmzmZBHWiYg2PmRHIEL20IyENJy/pdz0v04yzdi3e8gmLJ6OuJB9VRhphU/MrnKe/zuroXZW23IHMEWm3xP1YNoAunVyPm49cZLNp3jIcm8/dTVtFCLEtB+vnaI25Jn/1SIhgX9OO11o+5kO9iXg9XTN2pXCfvCbke0cusk2uRHjNrwx2P7om3bvjSpVC6Mp9m810v34pzw3H3flqDnZz/G9BSInZg/FfnAQW5kRw1qqp2DK9FTcesRD7T2txPgd36BfWDuHUpf3yeNtG++HTEDO86lw2SfUjxNk0v4Tggo2W7wQvoIsTPnEl7xtb59i+r5sydmnKg/CDtZ34+gJ/ZfETAcAPtYZj3mqHxBSFhvlAOIVCzCeG0iQ3LL9mQHcKG8dOqcYDB/WitcRb+E3HFIjdWklO/AmNKvMi+NzMuriS01jRHuzHLm/Rxz8/YSJ5AoosGUty10AzGCYY+HOMIZjTbgk0fgY5P0Isn57xtP0G8Ltdr+GFtz/AIy+85X0B8xz2c4l885C50uVC8Z2Z11GD+5+zst4xTYpT7Qw3V+GGwyXmHMIBX9k4jLVX3gPAwyuUq1O3cFlOL3tFYS7qPt4DAJjZUmUK5SI710xDc3khdtz4W+eyCGViMaD/s3uv7f7CIQ1nrBjEGSsGsfLynwNUr9dj5nfb6pJHJgBUF+Xi9fc/BmBpWUXhSxRS7aYYEPb1EH457bLsmeS16wIHc55IRKN5+7pO3/b0K1pKsaKlFPvfGTyddTwkO85vss6zevFSfBhyH7jDQv/C7OGP9BESj9e2lRXkYEVfA5585d2Y/RIRvqTpUR0m0uJx7LmEQsRcxQDsYfm+u2OR7TixjRGiB+dnS/lTGspx519e4PdwLENe+z4ghGCxR1i/IEQ1gt1BG4ixe6KyRVV+BH/Z1m9mERM5sKscb3y0F8cMBEuckggf79VvzinMWyLabkJI4EgcqVRe5kdC2DmrHosS0NomA3GCccrQJBzZX4XSgCZrEyXaQ9YIv6JmE9Djo775wcf4/J0P2/YVO3LZkvrKvgb89K8vmt/9aNTmtFfjiZffQSSkoSg3ghV9Dbjmt38LchuenYRTOcTlppV9DYLwa79HLwGIOjhn8AOW3w7NNQOOIdh9ZcMwzrz9T7bfWioKccqSPsx3iIBBAMxtD26IX5avCyLvfbRbiHwgmVRoxNU5zRzYuZv86sZ9TGGcHcufoiQviv56+1Ip3yZntlajt7YU/3pbtztngesPmdEuLwNn9iAzw6jetBMA0FaSg7//+xPHlKR+GKgMHgQ+VeYDbnb2rAx++/WDusvR7kPT5Ic7rr4eu4rkZgWmraQwcgXRfrNd+dUi2dFWqK3gWlz+95jlVZfnG+KcbMKaZpvs8pP4AmGAFs0emElPPHas1Zt2IhIiuGO7s4NdUH67ZTL+syfYi2S1wcSFiwKXEJHRkGZL4ZwKTM1vmtWG6TLoOqw3aFzq5CM2K42QwIKvflySCpRmJogC2xvWsfODRltVEWa0xs5+RQFB5j3bWO6dpve4BT3YNNRifp9ixHiVaYlntlT5SnErS4vMpz51En7FAV/sYEcsoywA3p2ELJaviN8B2u0c7H7kjjwEK/sbfUXacL2+MFyz+MjvfbzHdg8ycwgC95kwO7483xKU+PPMaddD3nxiDJSTJ5Xg+8fsa6aDNq/HXeLY+d1Y1FWLbbM6jN8I7j5pBQ6dqX8XteWsfHqwdPtv560dMj9/c2kLtk2uQHtpcoS6TMO0s5f8xmwOByr8Ce9fmtuYknS7rG3yZlVAsDBkrA3aEsm4HC5rznyPtXFqs+Qa3GchooSb5rcgapmxhIWJpJvD28iI0J+BpMxch6fUYSm7Mi9ihuuTUZUXRm2Bvd9KlsNbJvLxCNP8ZsbdZUYpUkuybHWVze84I6jZA0+XYBsKOAs8fMD4/ae14Jj5sY4o4kAGAAMN5bjh8AWeZTPDpnCXP3/9dPOz0/3x40JNUW5M+Vsrda1QvBmQeJg9td/xme13+OzOGFtsNiEIkvRhi5GSOd53NC8SRs+kEpy5YtDRYa3AFLjtS7UiTLDn74sJLmGNmEkN9hiaLJmtOGA3i/CKgHHL0Ytx85GLYq4nxgFe3FVr+95WkotzZzdMmM5N5NiBajQWRrG4IXZ5Oz8Swm2rO3DVkpbUF8wFS/Nrf+ZBsh/C7DOctbMAH2pL8quxabhZd6L89GJ7tjPbJJFN0DX7eWU0lBWY9xjW7E5yOZJ+kiE6ZRJijyrhFGHCqWnvGYlPcvaKX+vEHw7qxW83T7Ztsxze4jplRsM0v27C75X7NttSP48F8aQ3nigka94xUTS/WW324IT4cNcPNuGqXz9l3yg5z7cOnecaVYFFShCXMIMgC5jN47QEzwag2W3VOG/tEB7911vG/gQXHTgTHVWCQODRR7j1IZdtnoVHX3zb9/Id22/rjHZsndGOZZf+zPztv/YbwMKuSa4Ofoyrt85FOERQEA3j1of/kZAgf9kWPfzaIy+8Kf39kgNn4qHn30A0rGHvaDCzBz76AmNqQzlOWDQZ+zmkmvWbBQtAjNa4tiQfr773kWFfqZ+nubwQZ64cxIPPv+H7vOOdzrJc/EoQOHim13iv5qSCkEZibFrFyZ9b6uIYJO+q7N0ozo3g1fc+ktrZi/1OjE06912MXe1mA15VmGtpfkPEVq5ISMNwc6XZb/KI59SI3QY7iIhTlBvBiYv9pS4WOW24Fof3VWH2LU+iM8CKiaxvHEvN7+TyXEyvTl/7Zm3BLWTXipbSMS9Hr5EOeHtv6uyd045k1TsRlM3vOGM0QAMQ9yGEoKGsAC9ysX1l52n2ENCYh/WmoVbPMtywfQGiYQ1br/2VUBarTFcdPCcmPJiTgxgVNN9s8KKAFXuXO7/34OE8magvK0C9zJvcAbdHUpATxsIuf6md26p0h4I3P/jYOLHvIgCQ37NTe2koKzCfp5sHriyUGcuSxxeQEIJ1g7HLydbvjj95ctaqQTz8z7dQU5yHf7z5fszv795/EwDgvP/8AjtPPDX+C8VJMlYbUsWV+zbj2Xc/Seo5z7v8YrwT1fuOE0/biXNWT8PO/3sEgNVuRM1vEOHXWvWytsna07lrh/D7Xa+hujj23G99oN/zw8ZkUNSUyjIZatyKgxPRsGb+HtY0W7lCIYIvbxiWHnfq0n7c/NAu3PUE87sg4N/gIAq+9hd+jsfvvB/n7f4gcPvXCEF1fgQPbe0LlG1LhulHMQavgyzkWSq5eGETbnn6LfRVBJi0JcDs2kL84ZUPUJEbxqenWX4flXkR7Nox6HLkxCVZGtsMsVxJmKwRfkXhzw2Zlk3cFM+AXZIXxd0nrbBtG26uxA8eeR4DXIgwwIrHKWLZLsvz2zstwYtLakxDLC4D+b0rN+eYoCR7JsnuTUyX7ITb5f20Fzf7Sz7Ch5/9eTZObTazXsUTiotRnBvF4m6HCQQF/v27mwEA5/8OaRF+xxMrWkqxwnu3QJx/xSXm5xNP24nZbdU4Yk4nrv/9s+ZTZ5rfnLCGr27cJ8YBzA3zXfVoy5WFuY4TsOfeeA+A1Y8s6p6E+55+GS+9+yHeFRxDQwE0vyFNw+MvvQNAz9rGF9HNlr/GyBTJhF9R8xtE93vH9Rebn+Nt/+W5iQ+lyexTM436wij+a7o/JUYyuGZpC179cA/aSuLPPDnRSJbmd6IkmMka4XckwKxaJoyJDSdZs6ihpkrcdcKymFBGTngNZBEHswcnza/TuORlG5XMJbpkv0yl+VGcvWoqpjTEZ4/H48fcQGwb1x023xRW2E8yxzmvM39qobVEn/QOZ2L0XxMX4QVjfgI54RD6hImy96mcJ/79Ps8lvgfFuVFcsnkWTrj597rwK9H8hnxofnl2j4yagl9jWUHAiBb2fcVLjofmbsVjTWsxJgT5kRDaSuKPqzsRyRoHL59kjfCbiNkDoOedf/6tD8zvA/WJC1YMN8G3sjDXWsaHFXnC6S6cnKFGBeHfOS5xwJ7XpqmJrzn57ezXDzZhZmu1r33F8GczW6owQin+9E+5Da9X2bokWnaGOPDyCQTYb7zwIHNa9CyH6rmyCkv2tZs9yOxfPc8lS+phNL4FDmECRZxMe1hYwNyI1UDFiDRecZ8/vbgXl933JPaOjpr36ZaEyA/iFcdDJAUh4I5C4Ys713fi3U9is6PGoBqWjYSEX0LIhQDWAtgNYBeAIyil7yahXEknkNmDZJ9j5/dg7ZQmHPWd+wEAlYU5uPukFTbnLCfOWD7FMQmDyFkrB9FaaQXDvmH7ArvmxEPzG/YwexC1MiLxKhhvPXqxPZSSB/HEdj1+UXxOKYAeEeON9z/GIdf9yqVMsbB6WuRkNuCBLCkJM8uYEyAGcbIjMKh+MLMRE06wlQQxvq2/c8WuenUak7lGn7b5Tu3v9OUD+O2zr6G90pocsomak+b3qxuHccaPrJjdFUaEnJERirrSfBy/aLJr1sSEyOAlWxY2rcglm6MXTUXuaewVE48+j/CMZ8+qx/l/fMkxhXS2kqjm9x4AZ1JK9xJCLgBwJoDPJl6s5CNqPt2Q7RMOaWgqtxzagnjf79tT53tf0blL9PJm9+EkvDoNUmKoN6+MdF4rlWKSi7L8+GLDLu+tj9FWX7Zllu8sYUFw0p66PUm/2isnZPGlIyEN3ztykWv6ZZFk2QGy9sSH5FNkHqLZEXtfpzcFD5YvW/Va1luPnkmlnk66jN0OGufi3ChWDzTi3Q93m9tOXNxnu5747gwJ98D6Mmaatt7F8dMvmRbQakN7GV75z27XfU6cWoNJBRGsaSuN6xqPb+tPSexpxfhia08FtvZUpLsYGUdCwi+l9G7u6wMANiVWnLGD9d1uGrSO6mI89/p7vjyFebvghgCRDRIl3jCFoiexcyY4Y3+vDG9s/zg1KUyYWzcYm/e+h4s+kUzi0Z6y5yzL8ucHmac9EFz4TNaYVl9agDOWT8FwS/ozDjEGKvPw2JsfojQJTkMTDcKZEHz78AUoLwg+yZRlYySE+BZ8AW9zC759zm7TTZO8/AoYzKQiqRNeD0fehV2T8OtnXk3e9Ty4aGFsPyeSE9Zw6OT438t8l6xuCoXCTjJHmx0AbnX6kRByDIBjAKCpybsjSDZ+klyYmbB86A3Yeb55yNyUatHcnFfcYALlgg7dxs/JMc7vaS3P5PhIVUpbnniiSmiCViooMs1vPCTT4S3ISkQqOGtmHQ7oKkeLS0asbIOZxvArNLU+MkDK8BvtwY29HsKv7NyW5tf9WCYk+zHpuOrgOY4Jb07brx9fv+cJAN4C97aZHVg70IT3P9mDHRd4XlahUEwwPIVfQsi9AGReEWdRSu8w9jkLwF4ANzmdh1J6DYBrAGB4eDjlkk+QGIp+5BwmFPH2uakg3ixATeWFtjBrblnJAP9xfhNdjU9lWB+vwV8W4cLU/MYp/I4EsDV3Y6JmXQP0tLtTKuMT7CYqG6c14/1P9uCAaS0Jn4v1VbkJaAaZ5vebh8yVX0PSPP1qflncaz+aX1l4R8ay3gY8+uLbuPeplz3PAyAp0WAUCsX4xFP4pZQudfudELIdwBoAS2gG5w70Y/ZgJXjwr/lNNbVG7Nrh5sSWrZ00v36l2dltNZjbXoOj5yYWPD2VGmDHZ+byLFf0N+BXz76KFX0NcV3TXHFI0G5hrEz50qGBHyuuX9aKggmy9JsTDuHoeclJTDDYUI5tMzukJkZBcbJTD6r5veKg2ebnxnLdbGzNlGSuCFJctGkGfvPsq7jjsRescibxCgpFNnH54mY8+OoH3juOExKN9rACuoPbQkrph8kp0tjgx+xhPFBfVoBbj1qMUpc0yn5wColm4iET5UZC2LlmWkJlSDXxhAurLsrDdYfN97Vvd01JzLZkZW2aKIHFx5IFDc5awWxGIwTbZnUkdI4vrR/GL59+2TF9u6x5uml+u7h3RZb8xy9d1cV45vX3rHIY/ynVw1HWFOXZhF+FQhEfq1pLsaq1NN3FSBqJ2vxeASAHwD3G4PwApfS4hEs1BrgJIV/eMB0F0Qiu/s1Txr6pLFlwyuJwehFxConGbAwjCabq9EsqzR7GcuJz1wnLpAKq6Wmf4H2OpRN34eBy1BZEsFAJj2nhqM1b8V4ks80+misKccScLsffZe+WX5vfRLjwgBn498dWFAXWnzh14RunNuOy+55EBdeHbj7kKABA8Z6M1t8oFIokkmi0h8TUCSmE2ZPJOunh5ioAwGBDBZ569d9xh+0aTzgJggs6a/D8W204cHpriks09oyl8OuUqCRZZg9jqfmtWHEiZjcU4eplbWN2DYUzV59/AXYVpS7161ggm8RqPm1+EyEvGkZe1HkYEy+9ZkpTjHnF+RdeDQBof/+VZBdPoVBkKFmTN8qPo9j22Z24/rD5qCvNbC3MWBLSNBwxpwuFOYllWMpE0mHyMmpOulJ+6UBkePEUGY6sfbdXFiESItg6I3WTKubEFiSMm0KhyD6yJrCmmRzCLdSZRlCfwpi9yeT6w+bjwz17Ax+3JE1hrxrLCrDrjfeRl0IHJScBdCwFvzntNZjXUYOjkuC81FBWgE1DLYkXSqFIMrKViYKcCH5ywvKUlmNZbz2mN1WYWeMUCoVCRtYIv8mIdQkAA/VlePyld5JQouQSj9D+s08vT5vG75Sl/VjWW5/SyQZ79uEUqmFzIyGcszo5joF+He+CMNRUif0m1+HsgVhnPYXCL5nkj8kLvhlULIVCkUFkjfA7kiSv+69u3Mcz29F4IZ2RL/IiYdPWOpUcv2gyBlV8T5NISMN791yF8+/SnX2uPl9F/E81x539WdPhjdmfjjcyVcj0Y2589um6j3bxng9V+1cosoSsEX79mD34IRLSbFmXFOOL9YPNjr9lepSPseK2m/7H/KwG/9TzP7d9z/w8boXfTFL9SnArnmr/CkX2kTVSXLLMHhQTD9UkFAqFQqHIHrJG+AWASIhkvNe9IvVMb9Kz5c1tr0lzSRQKhUKhUIw1WWP2cPziDuzXM/Fi1yoSp7WyKO4MUwqFQqehNB8bp7WkuxgKhULhSdYIvwqFQqEYO67bviDdRYghL6qHUpzWWJHmkigUikxCCb8KhUKhmJAU50Zx/fb5qC7KS3dRFApFBqGEX4VCoVBMWOpLx2fiIoVCMXZklcObQqFQKBQKhSK7UcKvQqFQKBQKhSJrUMKvQqFQKBQKhSJrIDQNaa0IIW8A+GfKLwxUAngzDdcdr6j6Coaqr2Co+gqGqi//qLoKhqqvYKj6Cka66quZUlol+yEtwm+6IIT8iVI6nO5yjBdUfQVD1VcwVH0FQ9WXf1RdBUPVVzBUfQUjE+tLmT0oFAqFQqFQKLIGJfwqFAqFQqFQKLKGbBN+r0l3AcYZqr6CoeorGKq+gqHqyz+qroKh6isYqr6CkXH1lVU2vwqFQqFQKBSK7CbbNL8KhUKhUCgUiixGCb8KhUKhUCgUiqwha4RfQsgKQsjThJDnCCFnpLs86YYQ0kgIuY8Q8hQh5K+EkJOM7ecSQl4ihDxq/K3ijjnTqL+nCSHL01f69EAIeZ4Q8rhRL38ytpUTQu4hhDxr/C/j9s/a+iKEdHNt6FFCyHuEkJNV+7IghFxHCHmdEPIEty1weyKETDfa5XOEkMsIISTV95IKHOrrQkLI3wghfyGE/IgQUmpsbyGEfMS1s6u5Y7K5vgK/f9lQXw51dStXT88TQh41tqu25Sw/jJ/+i1I64f8AhADsAtAGIArgMQC96S5XmuukFsCQ8bkIwDMAegGcC+A0yf69Rr3lAGg16jOU7vtIcZ09D6BS2PY1AGcYn88AcIGqr5h6CwF4FUCzal+2e14AYAjAE4m0JwAPApgNgAD4KYCV6b63FNbXMgBh4/MFXH218PsJ58nm+gr8/mVDfcnqSvj9IgDnqLZl3qeT/DBu+q9s0fzOAPAcpfTvlNLdAG4BsD7NZUorlNJXKKWPGJ/fB/AUgHqXQ9YDuIVS+gml9B8AnoNer9nOegDfNj5/G8AGbruqL50lAHZRSt2yOmZdfVFKfwPgbWFzoPZECKkFUEwp/QPVR5IbuWMmFLL6opTeTSnda3x9AECD2zmyvb5cyOr25VZXhiZyM4Cb3c6RLXUFuMoP46b/yhbhtx7Av7jvL8Jd0MsqCCEtAKYB+KOx6QRjGfE6btlC1SFAAdxNCHmYEHKMsa2GUvoKoHcIAKqN7aq+LA6CfeBQ7cuZoO2p3vgsbs9GdkDXHDFaCSF/JoT8mhAy39im6ivY+6fqC5gP4DVK6bPcNtW2DAT5Ydz0X9ki/MpsSFSMNwCEkEIAPwRwMqX0PQDfANAOYCqAV6Av9wCqDgFgLqV0CMBKAMcTQha47KvqCwAhJApgHYDvG5tU+4oPp/pR9QaAEHIWgL0AbjI2vQKgiVI6DcCpAL5HCCmGqq+g71+21xcAHAz75F21LQOJ/OC4q2RbWttXtgi/LwJo5L43AHg5TWXJGAghEegN9yZK6f8CAKX0NUrpCKV0FMC3YC09Z30dUkpfNv6/DuBH0OvmNWPphi17vW7snvX1ZbASwCOU0tcA1b58ELQ9vQj7Un/W1RshZDuANQAOMZZOYSyvvmV8fhi6jWEXsry+4nj/srq+CCFhAPsDuJVtU21LRyY/YBz1X9ki/D4EoJMQ0mpoog4CcGeay5RWDDumawE8RSm9mNtey+22EQDzfr0TwEGEkBxCSCuATuiG6lkBIaSAEFLEPkN3tHkCer1sN3bbDuAO43NW1xeHTWui2pcngdqTsbT4PiFklvFOH8YdM+EhhKwA8FkA6yilH3LbqwghIeNzG/T6+ruqr2DvX7bXF4ClAP5GKTWX5lXbcpYfMJ76r1R41WXCH4BV0D0SdwE4K93lSfcfgHnQlxf+AuBR428VgO8AeNzYfieAWu6Ys4z6exoT1IvVpb7aoHurPgbgr6wNAagA8AsAzxr/y1V9mfefD+AtACXcNtW+rPu9GfoS6h7oGpAj42lPAIahCzG7AFwBI3PnRPtzqK/noNsSsj7samPfA4z39DEAjwBYq+oLR8bz/mVDfcnqyth+A4DjhH1V23KWH8ZN/6XSGysUCoVCoVAosoZsMXtQKBQKhUKhUCiU8KtQKBQKhUKhyB6U8KtQKBQKhUKhyBqU8KtQKBQKhUKhyBqU8KtQKBQKhUKhyBqU8KtQKBQKhUKhyBqU8KtQKBQKhUKhyBr+P+yLzgwd7IacAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Detect mean-shift on the second dimension\n", + "cost_function = CostL2OnSingleDim(dim=1)\n", + "algo_on_dim_1 = rpt.Window(width=WINDOW_SIZE, custom_cost=cost_function, jump=1).fit(signal_with_noise)\n", + "bkps_pred = algo_on_dim_1.predict(n_bkps=len(bkps_on_dim_1)-1) # the number of changes is known\n", + "# display signal and changes\n", + "fig, axes = rpt.display(signal_with_noise, bkps_on_dim_1, bkps_pred)\n", + "_ = axes[0].set_title((f\"\"\"Detection of mean-shifts using only Dimension 1:\\n\"\"\"n\n", + "f\"\"\"true changes: {bkps_on_dim_1[:-1]}, detected changes: {bkps_pred[:-1]}.\"\"\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe that, depending on the considered cost function (`CostL2OnSingleDim(dim=0)` or `CostL2OnSingleDim(dim=1)`), the detected changes are not the same even though the input signal is the same.\n", + "\n", + "Displaying the scores of the cost functions further illustrates the difference between the two costs. Recall that change-points correspond to high scores." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for dim, algo in enumerate([algo_on_dim_0, algo_on_dim_1]):\n", + " fig, ax = fig_ax()\n", + " ax.plot(np.r_[np.zeros(WINDOW_SIZE//2), algo.score, np.zeros(WINDOW_SIZE//2)])\n", + " ax.set_xmargin(0)\n", + " ax.set_title(f\"Score for CostL2OnSingleDim(dim={dim})\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "The question now for a user is how to combine the two cost functions to either detect all changes or only the changes that occur accros all dimensions simultaneously." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Detection with an aggregated cost function\n", + "### Intersection\n", + "\n", + "A first way of aggregating the scores is to consider each score along a dimension as being an expert. The idea is then to take the __intersection__ of the experts so that the predicted change points are the change points where all the experts are confident. This method is useful when the user is interested in collecting the change points that occur on all dimensions simultaneously. To that end, the scores are scaled to the [0, 1] range then the pointwise minimum is taken.\n", + "\n", + "In the following cell, the intersection procedure correctly predicts the only common change point between the two dimensions. The aggregated score shows clearly the only change point to predict." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# concatenate scores\n", + "score_arr = np.c_[algo_on_dim_0.score, algo_on_dim_1.score]\n", + "# intersection aggregation\n", + "algo_expert_intersection = rpt.Window(width=WINDOW_SIZE, jump=1).fit(signal_with_noise)\n", + "algo_expert_intersection.score = (minmax_scale(score_arr)).min(axis=1) # scaling + pointwise min\n", + "# only one change point is shared by both dimensions\n", + "bkps_intersection_predicted = algo_expert_intersection.predict(n_bkps=1)\n", + "# display the aggregated score\n", + "fig, ax = fig_ax()\n", + "ax.plot(np.r_[np.zeros(WINDOW_SIZE//2), algo_expert_intersection.score, np.zeros(WINDOW_SIZE//2)])\n", + "ax.set_xmargin(0)\n", + "_ = ax.set_title((\"\"\"Aggregated score (intersection of experts)\\n\"\"\"\n", + "f\"\"\"true change: {bkps_on_dim_1[0]}, detected change: {bkps_intersection_predicted[0]}\"\"\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Union\n", + "\n", + "Another way of aggregating the scores is to take the __union__ of the experts so that the predicted change points are the change points where at least one expert is confident. This method is useful when the user is interested in collecting all the change points that are present along all dimensions.\n", + "To that end, the scores are scaled to the [0, 1] range then the pointwise maximum is taken.\n", + "\n", + "In the following cell, the union procedure correctly predicts all the change points. The aggregated score shows 5 clear peaks from which to take the change points." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# union aggregation\n", + "# intersection aggregation\n", + "algo_expert_union = rpt.Window(width=WINDOW_SIZE, jump=1).fit(signal_with_noise)\n", + "algo_expert_union.score = minmax_scale(score_arr).max(axis=1) # scaling + pointwise max\n", + "# 5 change points are present\n", + "bkps_union_predicted = algo_expert_union.predict(n_bkps=5)\n", + "# display the aggregated score\n", + "fig, ax = fig_ax()\n", + "ax.plot(np.r_[np.zeros(WINDOW_SIZE//2), algo_expert_union.score, np.zeros(WINDOW_SIZE//2)])\n", + "ax.set_xmargin(0)\n", + "_ = ax.set_title((\"\"\"Aggregated score (intersection of experts)\\n\"\"\"\n", + "f\"\"\"true change: {bkps_all_dims[:-1]}, detected change: {bkps_union_predicted[:-1]}\"\"\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This example shows two ways to aggregate cost functions for a 2D toy signal which contains mean-shifts that do not always co-occur.:\n", + "- the [intersection of experts](#intersection) detects changes that are detected by all cost functions simultaneously,\n", + "- the [union of experts](#union) detects changes that are detected by at least one cost function.\n", + "\n", + "Those aggregation procedures can be applied to any set of cost functions and in a wide range of settings.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Authors\n", + "\n", + "This example notebook has been authored by [Théo VINCENT](https://github.com/theovincent) and edited by [Olivier Boulant](https://github.com/oboulant) and [Charles Truong](https://github.com/deepcharles)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[Katser2021]\n", + "Katser, I., Kozitsin, V., Lobachev, V., & Maksimov, I. (2021). Unsupervised Offline change point Detection Ensembles. Applied Sciences, 11(9), 4280." + ] + } + ], + "metadata": { + "interpreter": { + "hash": "de7c5fd504cc39d35018b8f7c7b90638a48a11123680d0b8595c478c27913e5d" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } From 658f2428b153451153de732eb21ea05877872cf5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 13 Apr 2022 20:02:15 +0000 Subject: [PATCH 17/24] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- docs/examples/ensemble-dimensions.ipynb | 2812 +++++++++++++++++++---- 1 file changed, 2371 insertions(+), 441 deletions(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index db17b240..33af4651 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -1,443 +1,2373 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Combining cost functions\n", - "\n", - "\n", - "\n", - "## Introduction\n", - "\n", - "In `ruptures`, change point detection procedures can only use a single cost function.\n", - "The choice of this cost function is crucial as it is related to the type of change to find. \n", - "For instance, [CostL2](../user-guide/costs/costl2.md) can detect shifts in the mean, [CostNormal](../user-guide/costs/costnormal.md) can detect shifts in the mean and the covariance structure, [CostAR](../user-guide/costs/costautoregressive.md) can detect shifts in the auto-regressive structure, etc.\n", - "\n", - "However, in many settings, several types of changes exist within a signal and a single cost function is not able to detect all changes simultaneously.\n", - "To cope with this issue, a procedure to combine cost functions is needed.\n", - "In this examples, a technique inspired by [[Katser2021]](#Katser2021) is presented.\n", - "In a nutshell, different costs are combined to yield an aggregated cost function which is sensitive to several types of changes.\n", - "The aggregated cost can then be used with the [window search method](../user-guide/detection/window.md) to create a change point detection algorithm.\n", - "\n", - "For simplicity, we focus on mean-shifts that occurr in different dimensions of a multivariate signal.\n", - "Each considered cost function can only detect mean-shift in a single dimension.\n", - "The user can choose between two aggregations procedures that can:\n", - "\n", - "- either detect shifts that occur simultaneously on all dimensions (intersection of experts),\n", - "- or detect shifts that occur on any dimension (union of experts).\n", - "\n", - "Here, only [CostL2](../user-guide/costs/costl2.md) is considered for all dimensions, but all other costs could be used. The focus is then on the way the costs are combined (see [intersection](#intersection) and [union](#union)).\n", - "In addition, the number of changes is assumed to be known by the user." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First, we make the necessary imports and a few define utility functions" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import ruptures as rpt\n", - "from ruptures.base import BaseCost\n", - "\n", - "WINDOW_SIZE = 200\n", - "\n", - "def minmax_scale(array: np.ndarray)->np.ndarray:\n", - " \"\"\"Scale each dimension to the [0, 1] range.\"\"\"\n", - " return (array - np.min(array, axis=0)) / (\n", - " np.max(array, axis=0) - np.min(array, axis=0) + 1e-8\n", - " )\n", - "\n", - "\n", - "def fig_ax(figsize=(10, 3)):\n", - " return plt.subplots(figsize=figsize)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The merging procedure of cost functions is applied on the following 2D toy signal, where each dimension contains three mean-shifts.\n", - "Note that only one mean-shift is shared by both dimensions and all other changes occur at different indexes." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]])\n", - "bkps_on_dim_0 = [329, 656, 1642, 2000]\n", - "bkps_on_dim_1 = [656, 972, 1291, 2000]\n", - "bkps_all_dims = [329, 656, 972, 1291, 1642, 2000]\n", - "\n", - "signal_with_noise = signal_no_noise + np.random.normal(size=signal_no_noise.shape)\n", - "\n", - "fig, axes = rpt.display(signal_no_noise, bkps_all_dims)\n", - "_ = axes[0].set_title(\"Toy signal (no noise)\")\n", - "\n", - "fig, axes = rpt.display(signal_with_noise, bkps_all_dims)\n", - "_ = axes[0].set_title(\"Toy signal (with noise)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Detection with a single cost function\n", - "\n", - "Consider the following cost function (derived from [CostL2](../user-guide/costs/costl2.md)) that can only detect mean-shift on one dimension at a time." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "class CostL2OnSingleDim(BaseCost):\n", - " \"\"\"This cost function detects mean-shift on a single dimension of a multivariate signal.\"\"\"\n", - "\n", - " # The 2 following attributes must be specified for compatibility.\n", - " model = \"CostL2OnSingleDim\"\n", - " min_size = 1\n", - "\n", - " def __init__(self, dim):\n", - " super().__init__()\n", - " self.dim = dim\n", - "\n", - " def fit(self, signal):\n", - " \"\"\"Set the internal parameter.\"\"\"\n", - " self.signal = signal[:, self.dim].reshape(-1, 1)\n", - " return self\n", - "\n", - " def error(self, start, end) -> float:\n", - " \"\"\"Return the approximation cost on the segment [start:end].\n", - "\n", - " Args:\n", - " start (int): start of the segment\n", - " end (int): end of the segment\n", - "\n", - " Returns:\n", - " segment cost\n", - "\n", - " Raises:\n", - " NotEnoughPoints: when the segment is too short (less than `min_size` samples).\n", - " \"\"\"\n", - " if end - start < self.min_size:\n", - " raise rpt.exceptions.NotEnoughPoints\n", - " if end - start == 1:\n", - " return 0.\n", - " return self.signal[start:end].var(axis=0).sum() * (end - start)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Combining this cost function with the [window search method](../user-guide/detection/window.md) yields the following change-points.\n", - "Note that the true changes are shown with the alternating colors and the estimated changes are shown with the vertical dashed lines." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Detect mean-shift on the first dimension\n", - "cost_function = CostL2OnSingleDim(dim=0)\n", - "algo_on_dim_0 = rpt.Window(width=WINDOW_SIZE, custom_cost=cost_function, jump=1).fit(signal_with_noise)\n", - "bkps_pred = algo_on_dim_0.predict(n_bkps=len(bkps_on_dim_0)-1) # the number of changes is known\n", - "# display signal and changes\n", - "fig, axes = rpt.display(signal_with_noise, bkps_on_dim_0, bkps_pred)\n", - "_ = axes[0].set_title((f\"\"\"Detection of mean-shifts using only Dimension 0:\\n\"\"\"\n", - "f\"\"\"true changes: {bkps_on_dim_0[:-1]}, detected changes: {bkps_pred[:-1]}.\"\"\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Detect mean-shift on the second dimension\n", - "cost_function = CostL2OnSingleDim(dim=1)\n", - "algo_on_dim_1 = rpt.Window(width=WINDOW_SIZE, custom_cost=cost_function, jump=1).fit(signal_with_noise)\n", - "bkps_pred = algo_on_dim_1.predict(n_bkps=len(bkps_on_dim_1)-1) # the number of changes is known\n", - "# display signal and changes\n", - "fig, axes = rpt.display(signal_with_noise, bkps_on_dim_1, bkps_pred)\n", - "_ = axes[0].set_title((f\"\"\"Detection of mean-shifts using only Dimension 1:\\n\"\"\"n\n", - "f\"\"\"true changes: {bkps_on_dim_1[:-1]}, detected changes: {bkps_pred[:-1]}.\"\"\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Observe that, depending on the considered cost function (`CostL2OnSingleDim(dim=0)` or `CostL2OnSingleDim(dim=1)`), the detected changes are not the same even though the input signal is the same.\n", - "\n", - "Displaying the scores of the cost functions further illustrates the difference between the two costs. Recall that change-points correspond to high scores." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for dim, algo in enumerate([algo_on_dim_0, algo_on_dim_1]):\n", - " fig, ax = fig_ax()\n", - " ax.plot(np.r_[np.zeros(WINDOW_SIZE//2), algo.score, np.zeros(WINDOW_SIZE//2)])\n", - " ax.set_xmargin(0)\n", - " ax.set_title(f\"Score for CostL2OnSingleDim(dim={dim})\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "The question now for a user is how to combine the two cost functions to either detect all changes or only the changes that occur accros all dimensions simultaneously." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Detection with an aggregated cost function\n", - "### Intersection\n", - "\n", - "A first way of aggregating the scores is to consider each score along a dimension as being an expert. The idea is then to take the __intersection__ of the experts so that the predicted change points are the change points where all the experts are confident. This method is useful when the user is interested in collecting the change points that occur on all dimensions simultaneously. To that end, the scores are scaled to the [0, 1] range then the pointwise minimum is taken.\n", - "\n", - "In the following cell, the intersection procedure correctly predicts the only common change point between the two dimensions. The aggregated score shows clearly the only change point to predict." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# concatenate scores\n", - "score_arr = np.c_[algo_on_dim_0.score, algo_on_dim_1.score]\n", - "# intersection aggregation\n", - "algo_expert_intersection = rpt.Window(width=WINDOW_SIZE, jump=1).fit(signal_with_noise)\n", - "algo_expert_intersection.score = (minmax_scale(score_arr)).min(axis=1) # scaling + pointwise min\n", - "# only one change point is shared by both dimensions\n", - "bkps_intersection_predicted = algo_expert_intersection.predict(n_bkps=1)\n", - "# display the aggregated score\n", - "fig, ax = fig_ax()\n", - "ax.plot(np.r_[np.zeros(WINDOW_SIZE//2), algo_expert_intersection.score, np.zeros(WINDOW_SIZE//2)])\n", - "ax.set_xmargin(0)\n", - "_ = ax.set_title((\"\"\"Aggregated score (intersection of experts)\\n\"\"\"\n", - "f\"\"\"true change: {bkps_on_dim_1[0]}, detected change: {bkps_intersection_predicted[0]}\"\"\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Union\n", - "\n", - "Another way of aggregating the scores is to take the __union__ of the experts so that the predicted change points are the change points where at least one expert is confident. This method is useful when the user is interested in collecting all the change points that are present along all dimensions.\n", - "To that end, the scores are scaled to the [0, 1] range then the pointwise maximum is taken.\n", - "\n", - "In the following cell, the union procedure correctly predicts all the change points. The aggregated score shows 5 clear peaks from which to take the change points." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# union aggregation\n", - "# intersection aggregation\n", - "algo_expert_union = rpt.Window(width=WINDOW_SIZE, jump=1).fit(signal_with_noise)\n", - "algo_expert_union.score = minmax_scale(score_arr).max(axis=1) # scaling + pointwise max\n", - "# 5 change points are present\n", - "bkps_union_predicted = algo_expert_union.predict(n_bkps=5)\n", - "# display the aggregated score\n", - "fig, ax = fig_ax()\n", - "ax.plot(np.r_[np.zeros(WINDOW_SIZE//2), algo_expert_union.score, np.zeros(WINDOW_SIZE//2)])\n", - "ax.set_xmargin(0)\n", - "_ = ax.set_title((\"\"\"Aggregated score (intersection of experts)\\n\"\"\"\n", - "f\"\"\"true change: {bkps_all_dims[:-1]}, detected change: {bkps_union_predicted[:-1]}\"\"\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "This example shows two ways to aggregate cost functions for a 2D toy signal which contains mean-shifts that do not always co-occur.:\n", - "- the [intersection of experts](#intersection) detects changes that are detected by all cost functions simultaneously,\n", - "- the [union of experts](#union) detects changes that are detected by at least one cost function.\n", - "\n", - "Those aggregation procedures can be applied to any set of cost functions and in a wide range of settings.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Authors\n", - "\n", - "This example notebook has been authored by [Théo VINCENT](https://github.com/theovincent) and edited by [Olivier Boulant](https://github.com/oboulant) and [Charles Truong](https://github.com/deepcharles)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "[Katser2021]\n", - "Katser, I., Kozitsin, V., Lobachev, V., & Maksimov, I. (2021). Unsupervised Offline change point Detection Ensembles. Applied Sciences, 11(9), 4280." - ] - } - ], - "metadata": { - "interpreter": { - "hash": "de7c5fd504cc39d35018b8f7c7b90638a48a11123680d0b8595c478c27913e5d" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Combining cost functions\n", + "\n", + "\n", + "\n", + "## Introduction\n", + "\n", + "In `ruptures`, change point detection procedures can only use a single cost function.\n", + "The choice of this cost function is crucial as it is related to the type of change to find. \n", + "For instance, [CostL2](../user-guide/costs/costl2.md) can detect shifts in the mean, [CostNormal](../user-guide/costs/costnormal.md) can detect shifts in the mean and the covariance structure, [CostAR](../user-guide/costs/costautoregressive.md) can detect shifts in the auto-regressive structure, etc.\n", + "\n", + "However, in many settings, several types of changes exist within a signal and a single cost function is not able to detect all changes simultaneously.\n", + "To cope with this issue, a procedure to combine cost functions is needed.\n", + "In this examples, a technique inspired by [[Katser2021]](#Katser2021) is presented.\n", + "In a nutshell, different costs are combined to yield an aggregated cost function which is sensitive to several types of changes.\n", + "The aggregated cost can then be used with the [window search method](../user-guide/detection/window.md) to create a change point detection algorithm.\n", + "\n", + "For simplicity, we focus on mean-shifts that occurr in different dimensions of a multivariate signal.\n", + "Each considered cost function can only detect mean-shift in a single dimension.\n", + "The user can choose between two aggregations procedures that can:\n", + "\n", + "- either detect shifts that occur simultaneously on all dimensions (intersection of experts),\n", + "- or detect shifts that occur on any dimension (union of experts).\n", + "\n", + "Here, only [CostL2](../user-guide/costs/costl2.md) is considered for all dimensions, but all other costs could be used. The focus is then on the way the costs are combined (see [intersection](#intersection) and [union](#union)).\n", + "In addition, the number of changes is assumed to be known by the user." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First, we make the necessary imports and a few define utility functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import ruptures as rpt\n", + "from ruptures.base import BaseCost\n", + "\n", + "WINDOW_SIZE = 200\n", + "\n", + "\n", + "def minmax_scale(array: np.ndarray) -> np.ndarray:\n", + " \"\"\"Scale each dimension to the [0, 1] range.\"\"\"\n", + " return (array - np.min(array, axis=0)) / (\n", + " np.max(array, axis=0) - np.min(array, axis=0) + 1e-8\n", + " )\n", + "\n", + "\n", + "def fig_ax(figsize=(10, 3)):\n", + " return plt.subplots(figsize=figsize)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The merging procedure of cost functions is applied on the following 2D toy signal, where each dimension contains three mean-shifts.\n", + "Note that only one mean-shift is shared by both dimensions and all other changes occur at different indexes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "signal_no_noise = np.array(\n", + " [\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " [0.0, 0.0],\n", + " 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[1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " [1.0, 1.0],\n", + " ]\n", + ")\n", + "bkps_on_dim_0 = [329, 656, 1642, 2000]\n", + "bkps_on_dim_1 = [656, 972, 1291, 2000]\n", + "bkps_all_dims = [329, 656, 972, 1291, 1642, 2000]\n", + "\n", + "signal_with_noise = signal_no_noise + np.random.normal(size=signal_no_noise.shape)\n", + "\n", + "fig, axes = rpt.display(signal_no_noise, bkps_all_dims)\n", + "_ = axes[0].set_title(\"Toy signal (no noise)\")\n", + "\n", + "fig, axes = rpt.display(signal_with_noise, bkps_all_dims)\n", + "_ = axes[0].set_title(\"Toy signal (with noise)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Detection with a single cost function\n", + "\n", + "Consider the following cost function (derived from [CostL2](../user-guide/costs/costl2.md)) that can only detect mean-shift on one dimension at a time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class CostL2OnSingleDim(BaseCost):\n", + " \"\"\"This cost function detects mean-shift on a single dimension of a multivariate signal.\"\"\"\n", + "\n", + " # The 2 following attributes must be specified for compatibility.\n", + " model = \"CostL2OnSingleDim\"\n", + " min_size = 1\n", + "\n", + " def __init__(self, dim):\n", + " super().__init__()\n", + " self.dim = dim\n", + "\n", + " def fit(self, signal):\n", + " \"\"\"Set the internal parameter.\"\"\"\n", + " self.signal = signal[:, self.dim].reshape(-1, 1)\n", + " return self\n", + "\n", + " def error(self, start, end) -> float:\n", + " \"\"\"Return the approximation cost on the segment [start:end].\n", + "\n", + " Args:\n", + " start (int): start of the segment\n", + " end (int): end of the segment\n", + "\n", + " Returns:\n", + " segment cost\n", + "\n", + " Raises:\n", + " NotEnoughPoints: when the segment is too short (less than `min_size` samples).\n", + " \"\"\"\n", + " if end - start < self.min_size:\n", + " raise rpt.exceptions.NotEnoughPoints\n", + " if end - start == 1:\n", + " return 0.0\n", + " return self.signal[start:end].var(axis=0).sum() * (end - start)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Combining this cost function with the [window search method](../user-guide/detection/window.md) yields the following change-points.\n", + "Note that the true changes are shown with the alternating colors and the estimated changes are shown with the vertical dashed lines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Detect mean-shift on the first dimension\n", + "cost_function = CostL2OnSingleDim(dim=0)\n", + "algo_on_dim_0 = rpt.Window(width=WINDOW_SIZE, custom_cost=cost_function, jump=1).fit(\n", + " signal_with_noise\n", + ")\n", + "bkps_pred = algo_on_dim_0.predict(\n", + " n_bkps=len(bkps_on_dim_0) - 1\n", + ") # the number of changes is known\n", + "# display signal and changes\n", + "fig, axes = rpt.display(signal_with_noise, bkps_on_dim_0, bkps_pred)\n", + "_ = axes[0].set_title(\n", + " (\n", + " f\"\"\"Detection of mean-shifts using only Dimension 0:\\n\"\"\"\n", + " f\"\"\"true changes: {bkps_on_dim_0[:-1]}, detected changes: {bkps_pred[:-1]}.\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Detect mean-shift on the second dimension\n", + "cost_function = CostL2OnSingleDim(dim=1)\n", + "algo_on_dim_1 = rpt.Window(width=WINDOW_SIZE, custom_cost=cost_function, jump=1).fit(signal_with_noise)\n", + "bkps_pred = algo_on_dim_1.predict(n_bkps=len(bkps_on_dim_1)-1) # the number of changes is known\n", + "# display signal and changes\n", + "fig, axes = rpt.display(signal_with_noise, bkps_on_dim_1, bkps_pred)\n", + "_ = axes[0].set_title((f\"\"\"Detection of mean-shifts using only Dimension 1:\\n\"\"\"n\n", + "f\"\"\"true changes: {bkps_on_dim_1[:-1]}, detected changes: {bkps_pred[:-1]}.\"\"\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe that, depending on the considered cost function (`CostL2OnSingleDim(dim=0)` or `CostL2OnSingleDim(dim=1)`), the detected changes are not the same even though the input signal is the same.\n", + "\n", + "Displaying the scores of the cost functions further illustrates the difference between the two costs. Recall that change-points correspond to high scores." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for dim, algo in enumerate([algo_on_dim_0, algo_on_dim_1]):\n", + " fig, ax = fig_ax()\n", + " ax.plot(np.r_[np.zeros(WINDOW_SIZE // 2), algo.score, np.zeros(WINDOW_SIZE // 2)])\n", + " ax.set_xmargin(0)\n", + " ax.set_title(f\"Score for CostL2OnSingleDim(dim={dim})\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "The question now for a user is how to combine the two cost functions to either detect all changes or only the changes that occur accros all dimensions simultaneously." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Detection with an aggregated cost function\n", + "### Intersection\n", + "\n", + "A first way of aggregating the scores is to consider each score along a dimension as being an expert. The idea is then to take the __intersection__ of the experts so that the predicted change points are the change points where all the experts are confident. This method is useful when the user is interested in collecting the change points that occur on all dimensions simultaneously. To that end, the scores are scaled to the [0, 1] range then the pointwise minimum is taken.\n", + "\n", + "In the following cell, the intersection procedure correctly predicts the only common change point between the two dimensions. The aggregated score shows clearly the only change point to predict." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# concatenate scores\n", + "score_arr = np.c_[algo_on_dim_0.score, algo_on_dim_1.score]\n", + "# intersection aggregation\n", + "algo_expert_intersection = rpt.Window(width=WINDOW_SIZE, jump=1).fit(signal_with_noise)\n", + "algo_expert_intersection.score = (minmax_scale(score_arr)).min(\n", + " axis=1\n", + ") # scaling + pointwise min\n", + "# only one change point is shared by both dimensions\n", + "bkps_intersection_predicted = algo_expert_intersection.predict(n_bkps=1)\n", + "# display the aggregated score\n", + "fig, ax = fig_ax()\n", + "ax.plot(\n", + " np.r_[\n", + " np.zeros(WINDOW_SIZE // 2),\n", + " algo_expert_intersection.score,\n", + " np.zeros(WINDOW_SIZE // 2),\n", + " ]\n", + ")\n", + "ax.set_xmargin(0)\n", + "_ = ax.set_title(\n", + " (\n", + " \"\"\"Aggregated score (intersection of experts)\\n\"\"\"\n", + " f\"\"\"true change: {bkps_on_dim_1[0]}, detected change: {bkps_intersection_predicted[0]}\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Union\n", + "\n", + "Another way of aggregating the scores is to take the __union__ of the experts so that the predicted change points are the change points where at least one expert is confident. This method is useful when the user is interested in collecting all the change points that are present along all dimensions.\n", + "To that end, the scores are scaled to the [0, 1] range then the pointwise maximum is taken.\n", + "\n", + "In the following cell, the union procedure correctly predicts all the change points. The aggregated score shows 5 clear peaks from which to take the change points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# union aggregation\n", + "# intersection aggregation\n", + "algo_expert_union = rpt.Window(width=WINDOW_SIZE, jump=1).fit(signal_with_noise)\n", + "algo_expert_union.score = minmax_scale(score_arr).max(axis=1) # scaling + pointwise max\n", + "# 5 change points are present\n", + "bkps_union_predicted = algo_expert_union.predict(n_bkps=5)\n", + "# display the aggregated score\n", + "fig, ax = fig_ax()\n", + "ax.plot(\n", + " np.r_[\n", + " np.zeros(WINDOW_SIZE // 2), algo_expert_union.score, np.zeros(WINDOW_SIZE // 2)\n", + " ]\n", + ")\n", + "ax.set_xmargin(0)\n", + "_ = ax.set_title(\n", + " (\n", + " \"\"\"Aggregated score (intersection of experts)\\n\"\"\"\n", + " f\"\"\"true change: {bkps_all_dims[:-1]}, detected change: {bkps_union_predicted[:-1]}\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This example shows two ways to aggregate cost functions for a 2D toy signal which contains mean-shifts that do not always co-occur.:\n", + "- the [intersection of experts](#intersection) detects changes that are detected by all cost functions simultaneously,\n", + "- the [union of experts](#union) detects changes that are detected by at least one cost function.\n", + "\n", + "Those aggregation procedures can be applied to any set of cost functions and in a wide range of settings.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Authors\n", + "\n", + "This example notebook has been authored by [Théo VINCENT](https://github.com/theovincent) and edited by [Olivier Boulant](https://github.com/oboulant) and [Charles Truong](https://github.com/deepcharles)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[Katser2021]\n", + "Katser, I., Kozitsin, V., Lobachev, V., & Maksimov, I. (2021). Unsupervised Offline change point Detection Ensembles. Applied Sciences, 11(9), 4280." + ] + } + ], + "metadata": { + "interpreter": { + "hash": "de7c5fd504cc39d35018b8f7c7b90638a48a11123680d0b8595c478c27913e5d" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } From eba433acb1a1ad6a0f74e6e041143cf359822b58 Mon Sep 17 00:00:00 2001 From: Charles T Date: Wed, 13 Apr 2022 22:05:32 +0200 Subject: [PATCH 18/24] docs(example): typo --- docs/examples/ensemble-dimensions.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 33af4651..313f39a7 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -37,7 +37,7 @@ "source": [ "## Setup\n", "\n", - "First, we make the necessary imports and a few define utility functions" + "First, we make the necessary imports and define a few utility functions." ] }, { From a3b9a87f1828ed69b669122b36ae8c2556246b96 Mon Sep 17 00:00:00 2001 From: Charles T Date: Wed, 13 Apr 2022 22:16:21 +0200 Subject: [PATCH 19/24] docs(example): typo + formatting --- docs/examples/ensemble-dimensions.ipynb | 2007 +---------------------- 1 file changed, 2 insertions(+), 2005 deletions(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 313f39a7..03dc7419 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -79,2010 +79,7 @@ "metadata": {}, "outputs": [], "source": [ - "signal_no_noise = np.array(\n", - " [\n", - " [0.0, 0.0],\n", - " [0.0, 0.0],\n", - " [0.0, 0.0],\n", - " 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[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]) # fmt: skip\n", "bkps_on_dim_0 = [329, 656, 1642, 2000]\n", "bkps_on_dim_1 = [656, 972, 1291, 2000]\n", "bkps_all_dims = [329, 656, 972, 1291, 1642, 2000]\n", @@ -2193,7 +190,7 @@ "bkps_pred = algo_on_dim_1.predict(n_bkps=len(bkps_on_dim_1)-1) # the number of changes is known\n", "# display signal and changes\n", "fig, axes = rpt.display(signal_with_noise, bkps_on_dim_1, bkps_pred)\n", - "_ = axes[0].set_title((f\"\"\"Detection of mean-shifts using only Dimension 1:\\n\"\"\"n\n", + "_ = axes[0].set_title((f\"\"\"Detection of mean-shifts using only Dimension 1:\\n\"\"\"\n", "f\"\"\"true changes: {bkps_on_dim_1[:-1]}, detected changes: {bkps_pred[:-1]}.\"\"\"))" ] }, From 5db321a21b069f164f2bdbae1972b371f6be4556 Mon Sep 17 00:00:00 2001 From: Charles T Date: Wed, 13 Apr 2022 22:26:13 +0200 Subject: [PATCH 20/24] docs: typo --- docs/examples/ensemble-dimensions.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 03dc7419..3abe6254 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -20,7 +20,7 @@ "In a nutshell, different costs are combined to yield an aggregated cost function which is sensitive to several types of changes.\n", "The aggregated cost can then be used with the [window search method](../user-guide/detection/window.md) to create a change point detection algorithm.\n", "\n", - "For simplicity, we focus on mean-shifts that occurr in different dimensions of a multivariate signal.\n", + "For simplicity, we focus on mean-shifts that occur in different dimensions of a multivariate signal.\n", "Each considered cost function can only detect mean-shift in a single dimension.\n", "The user can choose between two aggregations procedures that can:\n", "\n", From 85917a93d6e6739f569ebf8c8bdce55357feb69e Mon Sep 17 00:00:00 2001 From: Charles T Date: Wed, 13 Apr 2022 22:33:34 +0200 Subject: [PATCH 21/24] docs(example): typo --- docs/examples/ensemble-dimensions.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/ensemble-dimensions.ipynb index 3abe6254..2b1742aa 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/ensemble-dimensions.ipynb @@ -288,7 +288,6 @@ "outputs": [], "source": [ "# union aggregation\n", - "# intersection aggregation\n", "algo_expert_union = rpt.Window(width=WINDOW_SIZE, jump=1).fit(signal_with_noise)\n", "algo_expert_union.score = minmax_scale(score_arr).max(axis=1) # scaling + pointwise max\n", "# 5 change points are present\n", @@ -315,7 +314,8 @@ "source": [ "## Conclusion\n", "\n", - "This example shows two ways to aggregate cost functions for a 2D toy signal which contains mean-shifts that do not always co-occur.:\n", + "This example shows two ways to aggregate cost functions for a 2D toy signal which contains mean-shifts that do not always co-occur:\n", + "\n", "- the [intersection of experts](#intersection) detects changes that are detected by all cost functions simultaneously,\n", "- the [union of experts](#union) detects changes that are detected by at least one cost function.\n", "\n", From 18e24d38b00d711abe1c0540204098a58d2a2b8b Mon Sep 17 00:00:00 2001 From: Charles T Date: Wed, 13 Apr 2022 22:37:40 +0200 Subject: [PATCH 22/24] docs(example): change title --- mkdocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index ce86bea6..f54aa603 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -77,7 +77,7 @@ nav: - 'Simple usages': - 'Basic usage': examples/basic-usage.ipynb - 'Advanced usages': - - 'Dimensions ensemble model': examples/ensemble-dimensions.ipynb + - 'Combining cost functions': examples/ensemble-dimensions.ipynb - 'Kernel change point detection: a performance comparison': examples/kernel-cpd-performance-comparison.ipynb - 'Music segmentation': examples/music-segmentation.ipynb - 'Text segmentation': examples/text-segmentation.ipynb From af959c86b5ffe570192c70130e7654390f64526d Mon Sep 17 00:00:00 2001 From: Charles T Date: Thu, 14 Apr 2022 10:49:59 +0200 Subject: [PATCH 23/24] docs(example): renaming notebook + fmt --- ...ons.ipynb => merging-cost-functions.ipynb} | 51 +++++++++++++++---- mkdocs.yml | 2 +- 2 files changed, 41 insertions(+), 12 deletions(-) rename docs/examples/{ensemble-dimensions.ipynb => merging-cost-functions.ipynb} (95%) diff --git a/docs/examples/ensemble-dimensions.ipynb b/docs/examples/merging-cost-functions.ipynb similarity index 95% rename from docs/examples/ensemble-dimensions.ipynb rename to docs/examples/merging-cost-functions.ipynb index 2b1742aa..1fc78a45 100644 --- a/docs/examples/ensemble-dimensions.ipynb +++ b/docs/examples/merging-cost-functions.ipynb @@ -2,7 +2,10 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "id": "e07961b1-a84a-4749-ad7f-0d3fc617efd3", + "metadata": { + "tags": [] + }, "source": [ "# Combining cost functions\n", "\n", @@ -33,6 +36,7 @@ }, { "cell_type": "markdown", + "id": "dbdf7dce-47f1-415b-9d89-e2899b1f4e6c", "metadata": {}, "source": [ "## Setup\n", @@ -43,6 +47,7 @@ { "cell_type": "code", "execution_count": null, + "id": "0998be59-5a69-4617-b931-2c9d4fd6595d", "metadata": {}, "outputs": [], "source": [ @@ -67,6 +72,7 @@ }, { "cell_type": "markdown", + "id": "11388466-a41d-4e44-9789-0161e3cc5e88", "metadata": {}, "source": [ "The merging procedure of cost functions is applied on the following 2D toy signal, where each dimension contains three mean-shifts.\n", @@ -76,10 +82,13 @@ { "cell_type": "code", "execution_count": null, + "id": "84089dda-f638-42a2-a429-b5365860cd1f", "metadata": {}, "outputs": [], "source": [ - "signal_no_noise = np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 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1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]])\n", + "# fmt: on\n", "bkps_on_dim_0 = [329, 656, 1642, 2000]\n", "bkps_on_dim_1 = [656, 972, 1291, 2000]\n", "bkps_all_dims = [329, 656, 972, 1291, 1642, 2000]\n", @@ -95,6 +104,7 @@ }, { "cell_type": "markdown", + "id": "e43b33e1-f056-4d23-ad58-d7e5da754347", "metadata": {}, "source": [ "## Detection with a single cost function\n", @@ -105,6 +115,7 @@ { "cell_type": "code", "execution_count": null, + "id": "64e93a45-7a4c-42f8-b7aa-95365e831203", "metadata": { "tags": [] }, @@ -148,6 +159,7 @@ }, { "cell_type": "markdown", + "id": "78eaafde-ae84-4ff1-9c80-426040185d90", "metadata": {}, "source": [ "Combining this cost function with the [window search method](../user-guide/detection/window.md) yields the following change-points.\n", @@ -157,6 +169,7 @@ { "cell_type": "code", "execution_count": null, + "id": "63af12a7-bd4a-4732-92eb-a8599999a82c", "metadata": {}, "outputs": [], "source": [ @@ -181,21 +194,31 @@ { "cell_type": "code", "execution_count": null, + "id": "dfceeaa3-2c26-48ea-8af9-5cfa8d0fcdbe", "metadata": {}, "outputs": [], "source": [ "# Detect mean-shift on the second dimension\n", "cost_function = CostL2OnSingleDim(dim=1)\n", - "algo_on_dim_1 = rpt.Window(width=WINDOW_SIZE, custom_cost=cost_function, jump=1).fit(signal_with_noise)\n", - "bkps_pred = algo_on_dim_1.predict(n_bkps=len(bkps_on_dim_1)-1) # the number of changes is known\n", + "algo_on_dim_1 = rpt.Window(width=WINDOW_SIZE, custom_cost=cost_function, jump=1).fit(\n", + " signal_with_noise\n", + ")\n", + "bkps_pred = algo_on_dim_1.predict(\n", + " n_bkps=len(bkps_on_dim_1) - 1\n", + ") # the number of changes is known\n", "# display signal and changes\n", "fig, axes = rpt.display(signal_with_noise, bkps_on_dim_1, bkps_pred)\n", - "_ = axes[0].set_title((f\"\"\"Detection of mean-shifts using only Dimension 1:\\n\"\"\"\n", - "f\"\"\"true changes: {bkps_on_dim_1[:-1]}, detected changes: {bkps_pred[:-1]}.\"\"\"))" + "_ = axes[0].set_title(\n", + " (\n", + " f\"\"\"Detection of mean-shifts using only Dimension 1:\\n\"\"\"\n", + " f\"\"\"true changes: {bkps_on_dim_1[:-1]}, detected changes: {bkps_pred[:-1]}.\"\"\"\n", + " )\n", + ")" ] }, { "cell_type": "markdown", + "id": "ce63eac0-5cd5-474a-bafa-2b4c53425d57", "metadata": {}, "source": [ "Observe that, depending on the considered cost function (`CostL2OnSingleDim(dim=0)` or `CostL2OnSingleDim(dim=1)`), the detected changes are not the same even though the input signal is the same.\n", @@ -206,6 +229,7 @@ { "cell_type": "code", "execution_count": null, + "id": "03da2de4-9339-4280-afa8-b04d094213e4", "metadata": {}, "outputs": [], "source": [ @@ -218,6 +242,7 @@ }, { "cell_type": "markdown", + "id": "5b967d36-1dfa-42e8-a5e1-3085d4325624", "metadata": {}, "source": [ "\n", @@ -226,6 +251,7 @@ }, { "cell_type": "markdown", + "id": "7fb9ee7e-469e-4a11-a48b-6e3f152aa6d1", "metadata": {}, "source": [ "## Detection with an aggregated cost function\n", @@ -239,6 +265,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1b8ffd8d-8d41-4189-ba25-16ddaf09c063", "metadata": {}, "outputs": [], "source": [ @@ -271,6 +298,7 @@ }, { "cell_type": "markdown", + "id": "71c261aa-9287-4e47-b331-28ddb3a66c06", "metadata": {}, "source": [ "### Union\n", @@ -284,6 +312,7 @@ { "cell_type": "code", "execution_count": null, + "id": "f1571dca-0674-45d8-b9aa-e4200eed3e78", "metadata": {}, "outputs": [], "source": [ @@ -310,6 +339,7 @@ }, { "cell_type": "markdown", + "id": "ffff426b-2ab7-41fb-972c-2ef9ade72ed8", "metadata": {}, "source": [ "## Conclusion\n", @@ -325,6 +355,7 @@ }, { "cell_type": "markdown", + "id": "1a6d5256-67f3-4d3c-8192-4043ff6abce5", "metadata": {}, "source": [ "## Authors\n", @@ -334,6 +365,7 @@ }, { "cell_type": "markdown", + "id": "e100e594-eced-42db-9841-4bff2d737c1d", "metadata": {}, "source": [ "## References\n", @@ -344,9 +376,6 @@ } ], "metadata": { - "interpreter": { - "hash": "de7c5fd504cc39d35018b8f7c7b90638a48a11123680d0b8595c478c27913e5d" - }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", @@ -362,9 +391,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.13" } }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 5 } diff --git a/mkdocs.yml b/mkdocs.yml index f54aa603..f1dfacfd 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -77,7 +77,7 @@ nav: - 'Simple usages': - 'Basic usage': examples/basic-usage.ipynb - 'Advanced usages': - - 'Combining cost functions': examples/ensemble-dimensions.ipynb + - 'Combining cost functions': examples/merging-cost-functions.ipynb - 'Kernel change point detection: a performance comparison': examples/kernel-cpd-performance-comparison.ipynb - 'Music segmentation': examples/music-segmentation.ipynb - 'Text segmentation': examples/text-segmentation.ipynb From cd55775faadfbc8e33643d3f64fbec24210777b6 Mon Sep 17 00:00:00 2001 From: Charles T Date: Thu, 14 Apr 2022 11:08:19 +0200 Subject: [PATCH 24/24] docs(example): style --- docs/examples/merging-cost-functions.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/examples/merging-cost-functions.ipynb b/docs/examples/merging-cost-functions.ipynb index 1fc78a45..4e25e670 100644 --- a/docs/examples/merging-cost-functions.ipynb +++ b/docs/examples/merging-cost-functions.ipynb @@ -19,7 +19,7 @@ "\n", "However, in many settings, several types of changes exist within a signal and a single cost function is not able to detect all changes simultaneously.\n", "To cope with this issue, a procedure to combine cost functions is needed.\n", - "In this examples, a technique inspired by [[Katser2021]](#Katser2021) is presented.\n", + "In this example, a technique inspired by [[Katser2021]](#Katser2021) is presented.\n", "In a nutshell, different costs are combined to yield an aggregated cost function which is sensitive to several types of changes.\n", "The aggregated cost can then be used with the [window search method](../user-guide/detection/window.md) to create a change point detection algorithm.\n", "\n", @@ -31,7 +31,7 @@ "- or detect shifts that occur on any dimension (union of experts).\n", "\n", "Here, only [CostL2](../user-guide/costs/costl2.md) is considered for all dimensions, but all other costs could be used. The focus is then on the way the costs are combined (see [intersection](#intersection) and [union](#union)).\n", - "In addition, the number of changes is assumed to be known by the user." + "For simplicity, the number of changes is assumed to be known by the user." ] }, { @@ -257,7 +257,7 @@ "## Detection with an aggregated cost function\n", "### Intersection\n", "\n", - "A first way of aggregating the scores is to consider each score along a dimension as being an expert. The idea is then to take the __intersection__ of the experts so that the predicted change points are the change points where all the experts are confident. This method is useful when the user is interested in collecting the change points that occur on all dimensions simultaneously. To that end, the scores are scaled to the [0, 1] range then the pointwise minimum is taken.\n", + "A first way of aggregating the scores is to consider each score along a dimension as being an expert. The idea is to take the __intersection__ of the experts so that the predicted change points are the change points where all the experts are confident. This method is useful when the user is interested in collecting the change points that occur on all dimensions simultaneously. To that end, the scores are scaled to the [0, 1] range then the pointwise minimum is taken.\n", "\n", "In the following cell, the intersection procedure correctly predicts the only common change point between the two dimensions. The aggregated score shows clearly the only change point to predict." ]