diff --git a/aufgaben/p1/kennzahlen.ipynb b/aufgaben/p1/kennzahlen.ipynb
index b788a56b17556d665796493a9e7685e163009722..420082b9d2d152bb99c99b72b474f1387152fcbb 100644
--- a/aufgaben/p1/kennzahlen.ipynb
+++ b/aufgaben/p1/kennzahlen.ipynb
@@ -19,8 +19,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "start_time": "2023-04-23T23:18:09.952587Z",
- "end_time": "2023-04-23T23:18:10.173372Z"
+ "start_time": "2023-06-05T15:52:16.665218Z",
+ "end_time": "2023-06-05T15:52:16.852775Z"
}
}
},
@@ -442,13 +442,13 @@
}
],
"source": [
- "print(create_bewegung('gehen').get_bewegung_data())"
+ "print(create_bewegung('gehen').get_characterisation())"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "start_time": "2023-04-23T23:18:10.173372Z",
- "end_time": "2023-04-23T23:18:10.621223Z"
+ "start_time": "2023-06-05T15:52:16.852775Z",
+ "end_time": "2023-06-05T15:52:17.392881Z"
}
}
},
@@ -870,13 +870,13 @@
}
],
"source": [
- "print(create_bewegung('joggen').get_bewegung_data())"
+ "print(create_bewegung('joggen').get_characterisation())"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "start_time": "2023-04-23T23:18:10.621223Z",
- "end_time": "2023-04-23T23:18:11.195891Z"
+ "start_time": "2023-06-05T15:52:17.392881Z",
+ "end_time": "2023-06-05T15:52:17.918323Z"
}
}
},
@@ -1298,13 +1298,13 @@
}
],
"source": [
- "print(create_bewegung('jumpingjack').get_bewegung_data())"
+ "print(create_bewegung('jumpingjack').get_characterisation())"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "start_time": "2023-04-23T23:18:11.195891Z",
- "end_time": "2023-04-23T23:18:11.714719Z"
+ "start_time": "2023-06-05T15:52:17.918323Z",
+ "end_time": "2023-06-05T15:52:18.431863Z"
}
}
},
@@ -1726,13 +1726,13 @@
}
],
"source": [
- "print(create_bewegung('kniebeuge').get_bewegung_data())"
+ "print(create_bewegung('kniebeuge').get_characterisation())"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "start_time": "2023-04-23T23:18:11.714719Z",
- "end_time": "2023-04-23T23:18:12.209380Z"
+ "start_time": "2023-06-05T15:52:18.431863Z",
+ "end_time": "2023-06-05T15:52:18.974459Z"
}
}
},
@@ -2154,13 +2154,13 @@
}
],
"source": [
- "print(create_bewegung('treppe').get_bewegung_data())"
+ "print(create_bewegung('treppe').get_characterisation())"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "start_time": "2023-04-23T23:18:12.209380Z",
- "end_time": "2023-04-23T23:18:12.715561Z"
+ "start_time": "2023-06-05T15:52:18.974459Z",
+ "end_time": "2023-06-05T15:52:19.456134Z"
}
}
}
diff --git a/aufgaben/p10/evaluation.py b/aufgaben/p10/evaluation.py
index 69f5e507894d3aaa894ddced696e6e0844d0eafe..10a706e23c7d0f781c31a3a3ce4f1baae069bd7b 100644
--- a/aufgaben/p10/evaluation.py
+++ b/aufgaben/p10/evaluation.py
@@ -8,7 +8,7 @@ from algorithm.decision_tree.decision_tree import DecisionTree
from algorithm.k_nearest_neighbors.k_nearest_neighbors_algorithm import KNearestNeighborsAlgorithm
from aufgaben.p4.testdata import get_evaluation_data
from aufgaben.p6.error_rate import ErrorRate
-from aufgaben.p6.multiclass_error_rate import Multiclass_ErrorRate
+from aufgaben.p6.multiclass_error_rate import MulticlassErrorRate
from features.standard_deviation import standard_deviation
from features.arithmetic_mean import arithmetic_mean
from features.median import median
@@ -40,11 +40,11 @@ def evaluate():
# DecisionTree
print("\nDecision Tree:")
evaluate_algorithm(training_data, test_data,
- DecisionTree(entropy_threshold=0.5, number_segments=25, print_after_train=True), Multiclass_ErrorRate(classes))
+ DecisionTree(entropy_threshold=0.5, number_segments=25, print_after_train=True), MulticlassErrorRate(classes))
# KNN
print("\nKNN")
- evaluate_algorithm(training_data, test_data, KNearestNeighborsAlgorithm(), Multiclass_ErrorRate(classes), {'distance': euclidean_distance, 'k': 5})
+ evaluate_algorithm(training_data, test_data, KNearestNeighborsAlgorithm(), MulticlassErrorRate(classes), {'distance': euclidean_distance, 'k': 5})
# PLA
print("\nPLA")
@@ -52,7 +52,7 @@ def evaluate():
threshold = 0.5
perceptron = Perceptron(weights, threshold, numpy.tanh)
train(perceptron, training_data, 10000, 0.1)
- fehlerrate = Multiclass_ErrorRate(classes)
+ fehlerrate = MulticlassErrorRate(classes)
for features, correct_class in test_data:
result = perceptron.classify(features)
fehlerrate.evaluate(correct_class, result)
diff --git a/aufgaben/p3/plotter.py b/aufgaben/p3/plotter.py
index 73a67546eda9753610359fc3e7eb8ea4bcd6a973..c660198e392270610b184d9c7cd624b19b772504 100644
--- a/aufgaben/p3/plotter.py
+++ b/aufgaben/p3/plotter.py
@@ -1,6 +1,6 @@
+import plot.plotter as plotter
from features.moving_average import moving_average
from korpus import create_bewegung
-import plot.plotter as plotter
def test_plotter_sensoren():
diff --git a/aufgaben/p4/binary_classification.py b/aufgaben/p4/binary_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd92478684b6ddfdd10f08abb81ba2383eda9a5d
--- /dev/null
+++ b/aufgaben/p4/binary_classification.py
@@ -0,0 +1,33 @@
+import math
+
+from features.standard_deviation import standard_deviation
+from testdata.constants import Acc_Y
+from testdata.testdata import get_features, label_testdata
+
+CLASS_JOGGEN = 1
+CLASS_KNIEBEUGE = -1
+
+SENSOR_FUSS = 1
+
+
+def binary_classification_feature(window_size=30):
+ """ Testdaten für P4 und P5 """
+
+ personen = [
+ (1, 1) # Gruppe 1, Person 1
+ ]
+
+ # Standardabweichung in Y-Richtung sollte beim Joggen hoch und bei Kniebeuge gering sein
+ joggen_feature = get_features(standard_deviation, window_size, 'joggen', SENSOR_FUSS, Acc_Y, personen)
+ kniebeuge_feature = get_features(standard_deviation, window_size, 'kniebeuge', SENSOR_FUSS, Acc_Y, personen)
+
+ return joggen_feature, kniebeuge_feature
+
+
+def binary_classification_testdata():
+ joggen_feature, kniebeuge_feature = binary_classification_feature(150)
+
+ joggen_training, joggen_test = label_testdata(joggen_feature, CLASS_JOGGEN, math.inf)
+ kniebeuge_training, kniebeuge_test = label_testdata(kniebeuge_feature, CLASS_KNIEBEUGE, math.inf)
+
+ return joggen_training + kniebeuge_training, joggen_test + kniebeuge_test
diff --git a/aufgaben/p4/feature_plot.ipynb b/aufgaben/p4/feature_plot.ipynb
index 480414313f1bd04792d4f70a3d0fc71775216fde..882b3b13b12620459f86f790f73c71cf90a54512 100644
--- a/aufgaben/p4/feature_plot.ipynb
+++ b/aufgaben/p4/feature_plot.ipynb
@@ -2,12 +2,12 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {
"collapsed": true,
"ExecuteTime": {
- "start_time": "2023-05-08T13:40:15.203707Z",
- "end_time": "2023-05-08T13:40:17.815747Z"
+ "start_time": "2023-06-05T15:54:07.443928Z",
+ "end_time": "2023-06-05T15:54:09.651516Z"
}
},
"outputs": [
@@ -22,16 +22,16 @@
{
"data": {
"text/plain": "<IPython.core.display.HTML object>",
- "text/html": "<div id='79c3758f-e5f1-44b7-a889-2b1b07a9b1d2'></div>"
+ "text/html": "<div id='6170268f-673a-4725-9cf6-9ca12000f4f3'></div>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
+ "from aufgaben.p4.binary_classification import binary_classification_feature\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plotter\n",
- "from aufgaben.p4.testdata import binary_classification_feature\n",
"\n",
"joggen_feature, kniebeuge_feature = binary_classification_feature(150)\n",
"\n",
@@ -44,14 +44,14 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "start_time": "2023-05-08T13:40:17.815747Z",
- "end_time": "2023-05-08T13:40:17.843945Z"
+ "start_time": "2023-06-05T15:54:09.651516Z",
+ "end_time": "2023-06-05T15:54:09.678169Z"
}
}
}
diff --git a/aufgaben/p5/apply_pla.py b/aufgaben/p5/apply_pla.py
index 991f982da6459dbdc62e4e4a201563002c47dff8..85b13b20790f31983fa9ff94e813421f153b2a33 100644
--- a/aufgaben/p5/apply_pla.py
+++ b/aufgaben/p5/apply_pla.py
@@ -4,12 +4,13 @@ import numpy
from algorithm.pla.perceptron import Perceptron
from algorithm.pla.perceptron_learning_algorithm import train
-from aufgaben.p4.testdata import get_labeled_testdata, CLASS_JOGGEN, CLASS_KNIEBEUGE
+from aufgaben.p4.binary_classification import binary_classification_testdata
from aufgaben.p6.error_rate import ErrorRate
+from testdata.testdata import CLASS_JOGGEN, CLASS_KNIEBEUGE
def apply_pla():
- test_data, training_data = get_labeled_testdata()
+ test_data, training_data = binary_classification_testdata()
# Erstelle ein Perzeptron
weights = [random.random()]
diff --git a/aufgaben/p5/draw_results.ipynb b/aufgaben/p5/draw_results.ipynb
index 3fa3ee91bdf6664d6a9e6d303784247d32283491..290a4de20a51ffafc1ce8d71f7f17f33f4aa2bfe 100644
--- a/aufgaben/p5/draw_results.ipynb
+++ b/aufgaben/p5/draw_results.ipynb
@@ -6,23 +6,41 @@
"metadata": {
"collapsed": true,
"ExecuteTime": {
- "start_time": "2023-05-21T01:33:55.307530Z",
- "end_time": "2023-05-21T01:33:57.564890Z"
+ "start_time": "2023-06-05T15:54:37.919141Z",
+ "end_time": "2023-06-05T15:54:40.388783Z"
}
},
"outputs": [
{
- "ename": "TypeError",
- "evalue": "slice indices must be integers or None or have an __index__ method",
- "output_type": "error",
- "traceback": [
- "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)",
- "\u001B[1;32m<ipython-input-1-dc2cba0b48b6>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m 3\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mmatplotlib\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpyplot\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0mplotter\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 4\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 5\u001B[1;33m \u001B[0mjoggen\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mkniebeuge\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mgrenzwert\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mapply_pla\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 6\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 7\u001B[0m \u001B[0mplotter\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mscatter\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mjoggen\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m*\u001B[0m \u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mjoggen\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlabel\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m\"Joggen\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
- "\u001B[1;32mD:\\Informatik-Workspace\\PyCharm\\FH-Münster\\MEML\\aufgaben\\p5\\apply_pla.py\u001B[0m in \u001B[0;36mapply_pla\u001B[1;34m()\u001B[0m\n\u001B[0;32m 10\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 11\u001B[0m \u001B[1;32mdef\u001B[0m \u001B[0mapply_pla\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 12\u001B[1;33m \u001B[0mtest_data\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtraining_data\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mget_labeled_testdata\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 13\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 14\u001B[0m \u001B[1;31m# Erstelle ein Perzeptron\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
- "\u001B[1;32mD:\\Informatik-Workspace\\PyCharm\\FH-Münster\\MEML\\aufgaben\\p4\\testdata.py\u001B[0m in \u001B[0;36mget_labeled_testdata\u001B[1;34m()\u001B[0m\n\u001B[0;32m 13\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 14\u001B[0m \u001B[1;31m# Wir nehmen nur DATA_LIMIT an Daten (sonst ist K-Nearest-Neighbors zu langsam)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 15\u001B[1;33m \u001B[0mjoggen_feature\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mjoggen_feature\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mmin\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mDATA_LIMIT\u001B[0m\u001B[1;33m/\u001B[0m\u001B[1;36m2\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mjoggen_feature\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 16\u001B[0m \u001B[0mkniebeuge_feature\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mkniebeuge_feature\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mmin\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mDATA_LIMIT\u001B[0m \u001B[1;33m/\u001B[0m \u001B[1;36m2\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkniebeuge_feature\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 17\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
- "\u001B[1;31mTypeError\u001B[0m: slice indices must be integers or None or have an __index__ method"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Gesamt-Erfolgsrate: 1.0\n",
+ "Gesamt-Fehlerrate: 0.0\n",
+ "\n",
+ "Typ Absolut Relativ\n",
+ "-------------- --------- ---------\n",
+ "True-Positive 630 0.515548\n",
+ "True-Negative 592 0.484452\n",
+ "False-Positive 0 0\n",
+ "False-Negative 0 0\n"
]
+ },
+ {
+ "data": {
+ "text/plain": "<IPython.core.display.Javascript object>",
+ "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n var manager = IPython.notebook.keyboard_manager;\n if (!manager) {\n manager = IPython.keyboard_manager;\n }\n\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "<IPython.core.display.HTML object>",
+ "text/html": "<div id='da85e23d-8b51-4723-8359-8d708f1882d0'></div>"
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
@@ -42,14 +60,14 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "start_time": "2023-05-21T01:33:57.533653Z",
- "end_time": "2023-05-21T01:33:57.564890Z"
+ "start_time": "2023-06-05T15:54:40.379763Z",
+ "end_time": "2023-06-05T15:54:40.392790Z"
}
}
}
diff --git a/aufgaben/p6/multiclass_error_rate.py b/aufgaben/p6/multiclass_error_rate.py
index 0cecb3beebe7ed30cbddddabab2d47502e629987..a6571f3f706e395abf4ca4081faa8a58e366ace4 100644
--- a/aufgaben/p6/multiclass_error_rate.py
+++ b/aufgaben/p6/multiclass_error_rate.py
@@ -1,22 +1,20 @@
-from tabulate import tabulate
-
-
-class Multiclass_ErrorRate:
-
+class MulticlassErrorRate:
evaluations = {}
- def __init__(self, classes: list):
- for clazz in classes:
- self.evaluations[clazz] = [0, 0]
-
def evaluate(self, expected_class: float, actual_class: float):
+ if expected_class not in self.evaluations.keys():
+ self.evaluations[expected_class] = [0, 0]
+
if expected_class == actual_class:
self.evaluations[expected_class][0] += 1
else:
self.evaluations[expected_class][1] += 1
def print_table(self):
- for clazz in self.evaluations.keys():
- print(f"Klasse: {clazz}. Korrekt: {self.evaluations[clazz][0]}. Inkorrekt: {self.evaluations[clazz][1]}")
- error_rate = sum([clazz[1] for clazz in self.evaluations.values()]) / (sum([clazz[0] for clazz in self.evaluations.values()]) + sum([clazz[1] for clazz in self.evaluations.values()]))
+ for class_name in self.evaluations.keys():
+ print(
+ f"Klasse: {class_name}. Korrekt: {self.evaluations[class_name][0]}. Inkorrekt: {self.evaluations[class_name][1]}")
+ error_rate = sum([class_name[1] for class_name in self.evaluations.values()]) / (
+ sum([clazz[0] for clazz in self.evaluations.values()]) + sum(
+ [clazz[1] for clazz in self.evaluations.values()]))
print(f"Errorrate: {error_rate}")
diff --git a/aufgaben/p7/apply_k_nearest_neighbors.py b/aufgaben/p7/apply_k_nearest_neighbors.py
index 2ba80a5da08dc62d69ca562a77e1dff2ce8d3542..93892e2448f6e11ea099a6a9db27f10862ec3ec5 100644
--- a/aufgaben/p7/apply_k_nearest_neighbors.py
+++ b/aufgaben/p7/apply_k_nearest_neighbors.py
@@ -1,18 +1,18 @@
from algorithm.k_nearest_neighbors.distance_measure.euclidean_distance import euclidean_distance
from algorithm.k_nearest_neighbors.k_nearest_neighbors_algorithm import KNearestNeighborsAlgorithm
-from aufgaben.p4.testdata import get_labeled_testdata
-from aufgaben.p6.error_rate import ErrorRate
+from aufgaben.p6.multiclass_error_rate import MulticlassErrorRate
+from aufgaben.p7.multiple_class_classification import multiple_classes_testdata
def apply_k_nearest_neighbors():
- test_data, training_data = get_labeled_testdata()
+ test_data, training_data = multiple_classes_testdata()
# Trainiere den K-Nearest-Neighbors-Algorithm
algorithm = KNearestNeighborsAlgorithm()
algorithm.train(training_data)
# Vergleiche alle Ergebnisse mit der erwarteten Klasse
- fehlerrate = ErrorRate()
+ fehlerrate = MulticlassErrorRate()
for features, correct_class in test_data:
result = algorithm.classify(features, euclidean_distance, 10)
fehlerrate.evaluate(correct_class, result)
diff --git a/aufgaben/p7/multiple_class_classification.py b/aufgaben/p7/multiple_class_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cd44cfa9e7ab28d8264d554b6dbbe0bf896fe3e
--- /dev/null
+++ b/aufgaben/p7/multiple_class_classification.py
@@ -0,0 +1,33 @@
+from features.standard_deviation import standard_deviation
+from testdata.constants import Acc_Y
+from testdata.testdata import get_features, label_testdata
+
+SENSOR_FUSS = 1
+
+
+def multiple_classes_features(window_size=30):
+ """ Testdaten für P7 """
+
+ personen = [
+ (2, 1), # Gruppe 2, Person 1
+ (2, 2) # Gruppe 2, Person 2
+ ]
+
+ joggen_feature = get_features(standard_deviation, window_size, 'joggen', SENSOR_FUSS, Acc_Y, personen)
+ gehen_feature = get_features(standard_deviation, window_size, 'gehen', SENSOR_FUSS, Acc_Y, personen)
+ jumpingjack_feature = get_features(standard_deviation, window_size, 'jumpingjack', SENSOR_FUSS, Acc_Y, personen)
+
+ return joggen_feature, gehen_feature, jumpingjack_feature
+
+
+def multiple_classes_testdata():
+ joggen_feature, gehen_feature, jumpingjack_feature = multiple_classes_features(100)
+
+ joggen_training, joggen_test = label_testdata(joggen_feature, 'JOGGEN', 500)
+ gehen_training, gehen_test = label_testdata(joggen_feature, 'GEHEN', 500)
+ jumpingjack_training, jumpingjack_test = label_testdata(jumpingjack_feature, 'JUMPINGJACK', 500)
+
+ trainings_data = joggen_training + gehen_training + jumpingjack_training
+ test_data = joggen_test + gehen_test + jumpingjack_test
+
+ return trainings_data, test_data
diff --git a/aufgaben/p9/apply_decision_tree.py b/aufgaben/p9/apply_decision_tree.py
index fdc8ba4918539bad52d53189e4ee464b97e63467..762db887e9fbec7c5699749e5e75c517adfeeda8 100644
--- a/aufgaben/p9/apply_decision_tree.py
+++ b/aufgaben/p9/apply_decision_tree.py
@@ -4,7 +4,7 @@ import numpy
from algorithm.decision_tree.decision_tree import DecisionTree
from aufgaben.p4.testdata import get_labeled_testdata
-from aufgaben.p6.multiclass_error_rate import Multiclass_ErrorRate
+from aufgaben.p6.multiclass_error_rate import MulticlassErrorRate
def apply_decision_tree():
@@ -15,7 +15,7 @@ def apply_decision_tree():
algorithm.train(None, training_data)
# Vergleiche alle Ergebnisse mit der erwarteten Klasse
- fehlerrate = Multiclass_ErrorRate()
+ fehlerrate = MulticlassErrorRate()
for features, correct_class in test_data:
result = algorithm.classify(features)
fehlerrate.evaluate(correct_class, result)
diff --git a/testdata/__init__.py b/testdata/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/testdata/constants.py b/testdata/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9311a71f945e939c2f9027ce928a81709a74ada
--- /dev/null
+++ b/testdata/constants.py
@@ -0,0 +1,9 @@
+Acc_X = 0
+Acc_Y = 1
+Acc_Z = 2
+Gyr_X = 3
+Gyr_Y = 4
+Gyr_Z = 5
+Mag_X = 6
+Mag_Y = 7
+Mag_Z = 8
diff --git a/aufgaben/p4/testdata.py b/testdata/testdata.py
similarity index 50%
rename from aufgaben/p4/testdata.py
rename to testdata/testdata.py
index cb4ef8eef41d639361c8c1e059d4b1027dd6988c..4a7818d272c478beb89a020e16b5918b654350ba 100644
--- a/aufgaben/p4/testdata.py
+++ b/testdata/testdata.py
@@ -2,8 +2,7 @@ from math import floor
from features.moving_feature import moving_feature
from features.standard_deviation import standard_deviation
-from features.arithmetic_mean import arithmetic_mean
-from korpus import create_bewegung, create_bewegung_two_person
+from korpus import create_bewegung
CLASS_JOGGEN = 1
CLASS_KNIEBEUGE = -1
@@ -16,65 +15,49 @@ DATA_LIMIT = 1000
DATA_LIMIT_PER_TYPE = floor(DATA_LIMIT / 2)
-def binary_classification_feature(window_size=30):
- # Wir holen uns den Höhensensor des Fußes (Bleibt bei Kniebeugen gleich und bewegt sich beim Laufen)
- joggen_values = create_bewegung('joggen').messungen[1].sensoren[1].werte
- kniebeuge_values = create_bewegung('kniebeuge').messungen[1].sensoren[1].werte
- gehen_values = create_bewegung('gehen').messungen[1].sensoren[1].werte
+def get_features(feature_function, window_size, bewegungs_name, messung, sensor, personen: list):
+ values = []
- # Berechne die Standardabweichung (Bei Kniebeugen gering, bei Joggen hoch)
- joggen_feature = moving_feature(arithmetic_mean, window_size, joggen_values)
- kniebeuge_feature = moving_feature(arithmetic_mean, window_size, kniebeuge_values)
- gehen_feature = moving_feature(arithmetic_mean, window_size, gehen_values)
+ for person in personen:
+ bewegung = create_bewegung(bewegungs_name, gruppe=person[0], person=person[1])
+ values += list(bewegung.messungen[messung].sensoren[sensor].werte)
- return joggen_feature, kniebeuge_feature, gehen_feature
+ return moving_feature(feature_function, window_size, values)
def classification_evaluation(window_size=30, func=standard_deviation):
messung = 0
sensor = 0
- joggen_values = create_bewegung_two_person('joggen', person=1).messungen[messung].sensoren[sensor].werte
- kniebeuge_values = create_bewegung_two_person('gehen', person=1).messungen[messung].sensoren[sensor].werte
- jj_values = create_bewegung_two_person('jumpingjack', person=1).messungen[messung].sensoren[sensor].werte
- joggen_values2 = create_bewegung_two_person('joggen', person=2).messungen[messung].sensoren[sensor].werte
- kniebeuge_values2 = create_bewegung_two_person('gehen', person=2).messungen[messung].sensoren[sensor].werte
- jj_values2 = create_bewegung_two_person('jumpingjack', person=2).messungen[messung].sensoren[sensor].werte
+ joggen_values = create_bewegung('joggen', person=1).messungen[messung].sensoren[sensor].werte
+ kniebeuge_values = create_bewegung('gehen', person=1).messungen[messung].sensoren[sensor].werte
+ jj_values = create_bewegung('jumpingjack', person=1).messungen[messung].sensoren[sensor].werte
+ joggen_values2 = create_bewegung('joggen', person=2).messungen[messung].sensoren[sensor].werte
+ kniebeuge_values2 = create_bewegung('gehen', person=2).messungen[messung].sensoren[sensor].werte
+ jj_values2 = create_bewegung('jumpingjack', person=2).messungen[messung].sensoren[sensor].werte
# Berechne die Standardabweichung (Bei Kniebeugen gering, bei Joggen hoch)
- joggen_feature = moving_feature(func, window_size, list(joggen_values)+list(joggen_values2))
+ joggen_feature = moving_feature(func, window_size, list(joggen_values) + list(joggen_values2))
kniebeuge_feature = moving_feature(func, window_size, list(kniebeuge_values) + list(kniebeuge_values2))
jj_feature = moving_feature(func, window_size, list(jj_values) + list(jj_values2))
return joggen_feature, kniebeuge_feature, jj_feature
-def get_labeled_testdata():
- # Hole die aus den Sensordaten berechneten Merkmale
- joggen_feature, kniebeuge_feature, gehen_feature = binary_classification_feature(200)
-
+def label_testdata(features, label, data_limit):
# Wir nehmen nur DATA_LIMIT an Daten (sonst ist K-Nearest-Neighbors zu langsam)
- joggen_feature = joggen_feature[: min(DATA_LIMIT_PER_TYPE, len(joggen_feature))]
- kniebeuge_feature = kniebeuge_feature[: min(DATA_LIMIT_PER_TYPE, len(kniebeuge_feature))]
- gehen_feature = gehen_feature[: min(DATA_LIMIT_PER_TYPE, len(gehen_feature))]
+ features = features[: min(data_limit, len(features))]
# Wandel Liste an Merkmalen in einzelne Merkmalsvektoren um
- joggen_vector = ([element] for element in joggen_feature)
- kniebeugen_vector = ([element] for element in kniebeuge_feature)
- gehen_vector = ([element] for element in gehen_feature)
+ feature_vector = ([element] for element in features)
# Weise den Trainingsdaten eine Klasse zu
- # 0 = Kniebeuge, 1 = Joggen
- training_data_joggen = list(zip(joggen_vector, [CLASS_JOGGEN] * len(joggen_feature)))
- training_data_kniebeuge = list(zip(kniebeugen_vector, [CLASS_KNIEBEUGE] * len(kniebeuge_feature)))
- training_data_gehen = list(zip(gehen_vector, [CLASS_GEHEN] * len(gehen_feature)))
+ data = list(zip(feature_vector, [label] * len(features)))
# Wir nehmen 90 % der Testdaten zum Trainieren und 10 % zum Testen
- delimiter_joggen = floor(len(joggen_feature) * TRAINING_DATA_PERCENTAGE)
- delimiter_kniebeuge = floor(len(kniebeuge_feature) * TRAINING_DATA_PERCENTAGE)
- delimiter_gehen = floor(len(gehen_feature) * TRAINING_DATA_PERCENTAGE)
+ delimiter = floor(len(features) * TRAINING_DATA_PERCENTAGE)
- training_data = training_data_joggen[:delimiter_joggen] + training_data_kniebeuge[:delimiter_kniebeuge] + training_data_gehen[:delimiter_gehen]
- test_data = training_data_joggen[delimiter_joggen:] + training_data_kniebeuge[delimiter_kniebeuge:] + training_data_gehen[delimiter_gehen:]
+ training_data = data[:delimiter]
+ test_data = data[delimiter:]
return test_data, training_data
@@ -104,7 +87,6 @@ def get_evaluation_data(window_size, feature_list: []):
for i in range(len(jj_feature)):
jj_vector[i].append(jj_feature[i])
-
# Weise den Trainingsdaten eine Klasse zu
# 0 = Kniebeuge, 1 = Joggen
training_data_joggen = list(zip(joggen_vector, [CLASS_JOGGEN] * len(joggen_vector)))
@@ -116,8 +98,9 @@ def get_evaluation_data(window_size, feature_list: []):
delimiter_kniebeuge = floor(len(kniebeugen_vector) * TRAINING_DATA_PERCENTAGE)
delimiter_jj = floor(len(jj_vector) * TRAINING_DATA_PERCENTAGE)
- training_data = training_data_joggen[:delimiter_joggen] + training_data_kniebeuge[:delimiter_kniebeuge] + training_data_jj[:delimiter_jj]
- test_data = training_data_joggen[delimiter_joggen:] + training_data_kniebeuge[delimiter_kniebeuge:] + training_data_jj[delimiter_jj:]
+ training_data = training_data_joggen[:delimiter_joggen] + training_data_kniebeuge[
+ :delimiter_kniebeuge] + training_data_jj[:delimiter_jj]
+ test_data = training_data_joggen[delimiter_joggen:] + training_data_kniebeuge[
+ delimiter_kniebeuge:] + training_data_jj[delimiter_jj:]
return test_data, training_data
-