diff --git a/0-pilot-project/MNIST-kNN-Abgabe.ipynb b/0-pilot-project/MNIST-kNN-Abgabe.ipynb index 2c307e6..f127e85 100644 --- a/0-pilot-project/MNIST-kNN-Abgabe.ipynb +++ b/0-pilot-project/MNIST-kNN-Abgabe.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "879144d9", + "id": "8301251c", "metadata": {}, "source": [ "### Load MNIST dataset" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "bd032860", + "id": "3368e2c3", "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "30da011c", + "id": "0dc2fe45", "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "4f555050", + "id": "30459411", "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "e4de4331", + "id": "0be717f8", "metadata": {}, "outputs": [ { @@ -74,7 +74,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "b5221963", + "id": "ae9b3d51", "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "markdown", - "id": "811db75a", + "id": "7c89b6d3", "metadata": {}, "source": [ "### labels to int" @@ -92,7 +92,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "2bcc19ad", + "id": "30880538", "metadata": {}, "outputs": [], "source": [ @@ -104,7 +104,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "cc4b728f", + "id": "34b6be41", "metadata": {}, "outputs": [], "source": [ @@ -114,7 +114,7 @@ }, { "cell_type": "markdown", - "id": "d7113df3", + "id": "361fea4c", "metadata": {}, "source": [ "### Prepare data for machine learning" @@ -122,7 +122,7 @@ }, { "cell_type": "markdown", - "id": "570f328e", + "id": "cab5977a", "metadata": {}, "source": [ "### Identify Train Set and Test Set" @@ -131,7 +131,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "80e1ca03", + "id": "9bb80760", "metadata": {}, "outputs": [ { @@ -158,7 +158,7 @@ }, { "cell_type": "markdown", - "id": "ade8a1f6", + "id": "aac09882", "metadata": {}, "source": [ "## Pipeline Declaration" @@ -167,7 +167,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "bc5896c2", + "id": "ca389b56", "metadata": {}, "outputs": [], "source": [ @@ -195,7 +195,7 @@ }, { "cell_type": "markdown", - "id": "9e905584", + "id": "b7c97601", "metadata": {}, "source": [ "# Crossvalidation" @@ -204,7 +204,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "bbbb447c", + "id": "cd37833d", "metadata": {}, "outputs": [], "source": [ @@ -222,7 +222,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "4a8240c4", + "id": "f738a4ca", "metadata": {}, "outputs": [], "source": [ @@ -240,7 +240,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "f397cf42", + "id": "756c8015", "metadata": {}, "outputs": [], "source": [ @@ -263,7 +263,7 @@ }, { "cell_type": "markdown", - "id": "a543706f", + "id": "5d3b1484", "metadata": {}, "source": [ "# Fitting" @@ -272,7 +272,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "45452ceb", + "id": "1ea6a154", "metadata": {}, "outputs": [], "source": [ @@ -282,7 +282,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "03c01cd0", + "id": "ac4c7a18", "metadata": {}, "outputs": [ { @@ -333,7 +333,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "18f02d0c", + "id": "23c51b9e", "metadata": {}, "outputs": [ { @@ -385,7 +385,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "b2e7ee09", + "id": "8c92a008", "metadata": {}, "outputs": [ { @@ -437,7 +437,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "23ae34c3", + "id": "811c3930", "metadata": {}, "outputs": [ { @@ -490,7 +490,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "cac23616", + "id": "3c7440ff", "metadata": {}, "outputs": [ { @@ -543,7 +543,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "a57eb660", + "id": "8b491b79", "metadata": {}, "outputs": [ { @@ -596,7 +596,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "bcbedf38", + "id": "080ea6b8", "metadata": {}, "outputs": [ { @@ -649,7 +649,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "5bc4f44b", + "id": "6ee320cd", "metadata": {}, "outputs": [ { @@ -702,7 +702,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "a901ad5d", + "id": "17934567", "metadata": {}, "outputs": [ { @@ -755,7 +755,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "19e87457", + "id": "88fb14a4", "metadata": {}, "outputs": [ { @@ -816,7 +816,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "6146ccb1", + "id": "378c092b", "metadata": {}, "outputs": [ { @@ -877,7 +877,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "66a7637b", + "id": "3005da1d", "metadata": {}, "outputs": [ { @@ -930,7 +930,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "d4fadaac", + "id": "cbf8e245", "metadata": {}, "outputs": [ { @@ -983,7 +983,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "d15fb11c", + "id": "cc1c7c77", "metadata": {}, "outputs": [ { @@ -1035,7 +1035,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "2db8577b", + "id": "562c937f", "metadata": {}, "outputs": [ { @@ -1087,7 +1087,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "a5702428", + "id": "0c661938", "metadata": {}, "outputs": [], "source": [ @@ -1104,7 +1104,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "0ee57cfe", + "id": "05b7b881", "metadata": {}, "outputs": [], "source": [ @@ -1120,7 +1120,7 @@ }, { "cell_type": "markdown", - "id": "7fbbc930", + "id": "0a37b9d8", "metadata": {}, "source": [ "# Auswertung" @@ -1129,7 +1129,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "e5d609aa", + "id": "480adf73", "metadata": {}, "outputs": [ { @@ -1147,7 +1147,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "234f14bb", + "id": "202ff9a7", "metadata": {}, "outputs": [ { @@ -1179,7 +1179,7 @@ }, { "cell_type": "markdown", - "id": "a6ddb6f2", + "id": "94f1af95", "metadata": {}, "source": [ "Default n=3\\\n", @@ -1202,7 +1202,7 @@ }, { "cell_type": "markdown", - "id": "bde6e847", + "id": "99ad8309", "metadata": {}, "source": [ "n=3 euclid distance\\\n", @@ -1225,7 +1225,7 @@ }, { "cell_type": "markdown", - "id": "4c625bc3", + "id": "497d3216", "metadata": {}, "source": [ "# Hyper Parameter Optimization" @@ -1234,7 +1234,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "24ff7ea2", + "id": "e5e0c930", "metadata": {}, "outputs": [ { @@ -1269,7 +1269,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "b3b0eac3", + "id": "41349c36", "metadata": {}, "outputs": [], "source": [ @@ -1279,7 +1279,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "b68589fe", + "id": "91d2f4bc", "metadata": {}, "outputs": [], "source": [ @@ -1289,7 +1289,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b162e908", + "id": "c031b179", "metadata": {}, "outputs": [], "source": [] @@ -1311,7 +1311,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/0-pilot-project/MNIST-template.ipynb b/0-pilot-project/MNIST-template.ipynb index c5235d4..3270a35 100644 --- a/0-pilot-project/MNIST-template.ipynb +++ b/0-pilot-project/MNIST-template.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7a0c752a", + "id": "6904e7ae", "metadata": {}, "source": [ "### Load MNIST dataset" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "e07d82fe", + "id": "e3d41c8f", "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "1f97dcb1", + "id": "55990ccc", "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "01f83832", + "id": "933f52fb", "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "affa0e2b", + "id": "41435175", "metadata": {}, "outputs": [ { @@ -73,7 +73,7 @@ }, { "cell_type": "markdown", - "id": "4d51fd43", + "id": "fe14558d", "metadata": {}, "source": [ "Bunch objects are sometimes used as an output for functions and methods. They extend dictionaries by enabling values to be accessed by key, bunch[\"value_key\"], or by an attribute, bunch.value_key.\\\n", @@ -83,7 +83,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "78be57ab", + "id": "80a39a2e", "metadata": {}, "outputs": [ { @@ -106,7 +106,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "d0450c41", + "id": "5c211b9c", "metadata": {}, "outputs": [ { @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "e61e2adb", + "id": "a4c5fb8d", "metadata": {}, "source": [ "Datasets loaded by Scikit-Learn generally have a similar dictionary structure, including the following:\\\n", @@ -139,7 +139,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "fe285433", + "id": "7c077427", "metadata": {}, "outputs": [ { @@ -159,7 +159,7 @@ }, { "cell_type": "markdown", - "id": "5a70a746", + "id": "f3f2e42a", "metadata": {}, "source": [ "### Prepare the MNIST dataset" @@ -167,7 +167,7 @@ }, { "cell_type": "markdown", - "id": "a9b7a120", + "id": "6d5d2658", "metadata": {}, "source": [ "$f(X) = y$\n", @@ -181,7 +181,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "4e02cf2a", + "id": "99784cec", "metadata": {}, "outputs": [], "source": [ @@ -191,7 +191,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "001d736f", + "id": "923676c7", "metadata": {}, "outputs": [ { @@ -212,7 +212,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "b344be1d", + "id": "ed44fc7a", "metadata": {}, "outputs": [ { @@ -233,7 +233,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "cef23e9f", + "id": "94ee3e59", "metadata": {}, "outputs": [ { @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "fe3b1259", + "id": "478cb336", "metadata": {}, "source": [ "### Plot data" @@ -262,7 +262,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "953d9415", + "id": "f3cfebc6", "metadata": {}, "outputs": [], "source": [ @@ -274,7 +274,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "b68f6cee", + "id": "6d799c25", "metadata": {}, "outputs": [ { @@ -297,7 +297,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "8779b1a2", + "id": "72c7305b", "metadata": {}, "outputs": [ { @@ -317,7 +317,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "dcc605cf", + "id": "f5b0b349", "metadata": {}, "outputs": [ { @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "6d41d752", + "id": "c70641f8", "metadata": {}, "outputs": [ { @@ -364,7 +364,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "230cfd35", + "id": "98f8561b", "metadata": {}, "outputs": [], "source": [ @@ -375,7 +375,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "25a3a2e7", + "id": "33e244b2", "metadata": {}, "outputs": [], "source": [ @@ -389,7 +389,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "f1552762", + "id": "20043b74", "metadata": {}, "outputs": [ { @@ -413,7 +413,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "74b3a063", + "id": "6e4369de", "metadata": {}, "outputs": [], "source": [ @@ -429,7 +429,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "949b3914", + "id": "fb3f6d95", "metadata": {}, "outputs": [ { @@ -454,7 +454,7 @@ }, { "cell_type": "markdown", - "id": "ec8a9d34", + "id": "3a69d0fd", "metadata": {}, "source": [ "### Prepare data for machine learning" @@ -463,7 +463,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "febbd286", + "id": "dcc31672", "metadata": {}, "outputs": [ { @@ -485,7 +485,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "fff839b6", + "id": "282ba914", "metadata": {}, "outputs": [], "source": [ @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "2bdbeb4e", + "id": "5c34bbd5", "metadata": {}, "source": [ "### Train classifier" @@ -504,7 +504,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "4c32ae9f", + "id": "f0b6e1c9", "metadata": {}, "outputs": [], "source": [ @@ -515,7 +515,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "fe06ae55", + "id": "54e3fb64", "metadata": {}, "outputs": [ { @@ -540,7 +540,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "e6209258", + "id": "29446c32", "metadata": {}, "outputs": [ { @@ -565,7 +565,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "62773b1b", + "id": "5030ccc3", "metadata": {}, "outputs": [ { @@ -584,7 +584,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "0ce21474", + "id": "22c780fb", "metadata": {}, "outputs": [ { @@ -603,7 +603,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "78a8e8a7", + "id": "990d3925", "metadata": {}, "outputs": [ { @@ -626,7 +626,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "45d93a99", + "id": "b6e1d70c", "metadata": {}, "outputs": [ { @@ -647,7 +647,7 @@ }, { "cell_type": "markdown", - "id": "fc739051", + "id": "a65ed630", "metadata": {}, "source": [ "### Evaluation" @@ -656,7 +656,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "990a5b7c", + "id": "4c93c5f9", "metadata": {}, "outputs": [ { @@ -677,7 +677,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "f125a37d", + "id": "f01ec3dd", "metadata": {}, "outputs": [ { @@ -697,7 +697,7 @@ }, { "cell_type": "markdown", - "id": "bdcb6e6e", + "id": "c41db6b0", "metadata": {}, "source": [ "Accuracy is strongly influenced by the distribution of the classes in the test data." @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "be858cd5", + "id": "3f830f36", "metadata": {}, "source": [ "#### Cross Validation\n", @@ -715,7 +715,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "7adb1ea7", + "id": "eeec5311", "metadata": {}, "outputs": [ { @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "11d22c5e", + "id": "d1ed46a3", "metadata": {}, "outputs": [ { @@ -759,7 +759,7 @@ }, { "cell_type": "markdown", - "id": "b54e83a5", + "id": "539cfa0c", "metadata": {}, "source": [ "#### Precision" @@ -768,7 +768,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "ef7a9e7e", + "id": "abfe8383", "metadata": {}, "outputs": [ { @@ -790,7 +790,7 @@ }, { "cell_type": "markdown", - "id": "da723740", + "id": "d899dd6f", "metadata": {}, "source": [ "#### Recall" @@ -799,7 +799,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "cb77bf58", + "id": "15d30ae5", "metadata": {}, "outputs": [ { @@ -821,7 +821,7 @@ }, { "cell_type": "markdown", - "id": "28867d1b", + "id": "393c3b1c", "metadata": {}, "source": [ "#### F1 Score" @@ -830,7 +830,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "0674e0de", + "id": "53fa1823", "metadata": {}, "outputs": [ { @@ -852,7 +852,7 @@ }, { "cell_type": "markdown", - "id": "da59da11", + "id": "08b6bdc2", "metadata": {}, "source": [ "#### Confusion Matrix" @@ -861,7 +861,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "adbdeece", + "id": "e205d359", "metadata": {}, "outputs": [ { @@ -891,7 +891,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "fb50c5a4", + "id": "4ec777ac", "metadata": {}, "outputs": [ { @@ -929,7 +929,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "2f0d536a", + "id": "9c53a0a7", "metadata": {}, "outputs": [], "source": [ @@ -940,7 +940,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "dddf5fe8", + "id": "c47e7c69", "metadata": {}, "outputs": [ { @@ -970,7 +970,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "44537aae", + "id": "ef09fc40", "metadata": {}, "outputs": [ { @@ -1006,7 +1006,7 @@ { "cell_type": "code", "execution_count": null, - "id": "57d96f56", + "id": "6f9816f1", "metadata": {}, "outputs": [], "source": [] @@ -1028,7 +1028,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/1-first-project/ies/NeuralNetwork.ipynb b/1-first-project/ies/NeuralNetwork.ipynb index 74bc635..4504999 100644 --- a/1-first-project/ies/NeuralNetwork.ipynb +++ b/1-first-project/ies/NeuralNetwork.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "bfe7a783", + "id": "9f0ddabb", "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "c6c356ef", + "id": "1e8e6ffb", "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "69ae3c7a", + "id": "39cf526d", "metadata": {}, "outputs": [], "source": [ @@ -48,7 +48,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "e28642ea", + "id": "2b169e1f", "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "6ec561b1", + "id": "9a507393", "metadata": {}, "outputs": [], "source": [ @@ -89,7 +89,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "e975b7b9", + "id": "8d8da08e", "metadata": {}, "outputs": [ { @@ -121,7 +121,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "7b7f497c", + "id": "700db412", "metadata": {}, "outputs": [ { @@ -147,7 +147,7 @@ }, { "cell_type": "markdown", - "id": "55f47334", + "id": "16cdb8f5", "metadata": {}, "source": [ "**How to fix this error**:\n", @@ -160,7 +160,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "7f9bb477", + "id": "1f411bea", "metadata": {}, "outputs": [ { @@ -647,7 +647,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "5d10cf20", + "id": "1a8beb73", "metadata": {}, "outputs": [], "source": [ @@ -675,7 +675,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "0e97563e", + "id": "0dcd7db2", "metadata": {}, "outputs": [ { @@ -703,7 +703,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "2676a564", + "id": "3a0e96ac", "metadata": {}, "outputs": [], "source": [ @@ -714,7 +714,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "a26a60ff", + "id": "d2d8e64d", "metadata": {}, "outputs": [], "source": [ @@ -724,7 +724,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "39f3ef0e", + "id": "77c502cd", "metadata": {}, "outputs": [], "source": [ @@ -753,7 +753,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "1fa16a8f", + "id": "7235d56c", "metadata": {}, "outputs": [], "source": [ @@ -769,7 +769,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "eee9d775", + "id": "cc983f80", "metadata": {}, "outputs": [ { @@ -809,7 +809,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "27ae5c5a", + "id": "59d6ebca", "metadata": {}, "outputs": [ { @@ -874,7 +874,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "d2b3422e", + "id": "f4032731", "metadata": {}, "outputs": [ { @@ -946,7 +946,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "453149ff", + "id": "e20cb3cd", "metadata": {}, "outputs": [ { @@ -989,7 +989,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "dbf863e2", + "id": "5f88c9c6", "metadata": {}, "outputs": [], "source": [ @@ -1013,7 +1013,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/1-first-project/ies/Tensor.ipynb b/1-first-project/ies/Tensor.ipynb index fbb7e66..db766b5 100644 --- a/1-first-project/ies/Tensor.ipynb +++ b/1-first-project/ies/Tensor.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "9706d045", + "id": "024b33fe", "metadata": {}, "outputs": [], "source": [ @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "77388c0f", + "id": "72397786", "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "0fd516ff", + "id": "5f67ba6c", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "389e84c0", + "id": "805421f9", "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "197a57c5", + "id": "a6a00ee3", "metadata": {}, "outputs": [], "source": [ @@ -78,7 +78,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "e456ab87", + "id": "877573a2", "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cc789523", + "id": "93303e1e", "metadata": {}, "outputs": [], "source": [] @@ -125,7 +125,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "c59a84aa", + "id": "5eb3f0c1", "metadata": {}, "outputs": [], "source": [ @@ -141,7 +141,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "0c0a4649", + "id": "26bcaaad", "metadata": {}, "outputs": [], "source": [ @@ -196,7 +196,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "5a618dd4", + "id": "32584756", "metadata": {}, "outputs": [], "source": [ @@ -212,7 +212,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "52e5cc13", + "id": "e7056b09", "metadata": {}, "outputs": [], "source": [ @@ -229,7 +229,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "62463866", + "id": "fad8741d", "metadata": {}, "outputs": [], "source": [ @@ -241,7 +241,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "57ec862e", + "id": "96abe0b6", "metadata": {}, "outputs": [], "source": [ @@ -295,7 +295,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "cc2a6529", + "id": "2ec14876", "metadata": {}, "outputs": [], "source": [ @@ -306,7 +306,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "66975d62", + "id": "93d7b7e8", "metadata": {}, "outputs": [ { @@ -338,7 +338,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "cc664cf6", + "id": "bdd09b3a", "metadata": {}, "outputs": [ { @@ -360,7 +360,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "d8463d51", + "id": "b4b048c3", "metadata": {}, "outputs": [ { @@ -384,7 +384,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bbb07d9b", + "id": "d06cbda8", "metadata": {}, "outputs": [], "source": [] @@ -406,7 +406,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/1-first-project/ies/Tensor_v2.ipynb b/1-first-project/ies/Tensor_v2.ipynb index 45394ea..75c963b 100644 --- a/1-first-project/ies/Tensor_v2.ipynb +++ b/1-first-project/ies/Tensor_v2.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "43a8508f", + "id": "3168ef63", "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "650ab8d8", + "id": "ce4d9517", "metadata": {}, "outputs": [], "source": [ @@ -54,7 +54,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "7e5bbbe7", + "id": "5ed60433", "metadata": {}, "outputs": [], "source": [ @@ -77,7 +77,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "e2c35751", + "id": "9fefd9fa", "metadata": {}, "outputs": [], "source": [ @@ -135,7 +135,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "6a0e84e0", + "id": "b6c30ab7", "metadata": {}, "outputs": [], "source": [ @@ -155,7 +155,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "8a76bd49", + "id": "20cb0504", "metadata": {}, "outputs": [], "source": [ @@ -211,7 +211,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "95dbd528", + "id": "ec39550d", "metadata": {}, "outputs": [], "source": [ @@ -230,7 +230,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "f68432e1", + "id": "63509028", "metadata": {}, "outputs": [], "source": [ @@ -240,7 +240,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "48d57ee7", + "id": "f8dbeac5", "metadata": {}, "outputs": [], "source": [ @@ -250,7 +250,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "c8cf46a6", + "id": "b479a77c", "metadata": {}, "outputs": [ { @@ -268,7 +268,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "016a11f0", + "id": "3c7919a6", "metadata": {}, "outputs": [], "source": [ @@ -278,7 +278,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "77b0373e", + "id": "9592e2ca", "metadata": {}, "outputs": [ { @@ -296,7 +296,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "d9f3bf08", + "id": "7f294d3c", "metadata": {}, "outputs": [ { @@ -321,7 +321,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "25729dda", + "id": "fda2e601", "metadata": {}, "outputs": [ { @@ -61840,7 +61840,7 @@ { "cell_type": "code", "execution_count": null, - "id": "303c0850", + "id": "d1125102", "metadata": {}, "outputs": [], "source": [] @@ -61862,7 +61862,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/1-first-project/ies/cutting_Data.ipynb b/1-first-project/ies/cutting_Data.ipynb index 987b256..fe6e218 100644 --- a/1-first-project/ies/cutting_Data.ipynb +++ b/1-first-project/ies/cutting_Data.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "30758151", + "id": "7528c5d7", "metadata": {}, "outputs": [], "source": [ @@ -19,7 +19,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "1365458a", + "id": "5aa0ab63", "metadata": {}, "outputs": [ { @@ -94,7 +94,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "cef98b83", + "id": "c0453d68", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "86f1c9b4", + "id": "685f2966", "metadata": {}, "outputs": [], "source": [ @@ -146,7 +146,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "ab518f47", + "id": "57b2847d", "metadata": {}, "outputs": [ { @@ -192,7 +192,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a0c1292b", + "id": "5784fe87", "metadata": {}, "outputs": [], "source": [] @@ -214,7 +214,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/2-second-project/iel/Week1 /Test1.ipynb b/2-second-project/iel/Week1 /Test1.ipynb new file mode 100644 index 0000000..3a82f62 --- /dev/null +++ b/2-second-project/iel/Week1 /Test1.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "2288179b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 768/768 [01:11<00:00, 10.72it/s]\n" + ] + } + ], + "source": [ + "import os\n", + "from glob import glob\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.decomposition import PCA, KernelPCA\n", + "from sklearn.preprocessing import (StandardScaler, \n", + " MinMaxScaler, \n", + " MaxAbsScaler,\n", + " PowerTransformer,\n", + " Binarizer)\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.metrics import classification_report, accuracy_score\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from math import isqrt\n", + "import pickle\n", + "from tqdm import tqdm\n", + "import os\n", + "\n", + "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '2'\n", + "\n", + "def load_data(user_filter=None):\n", + " dic_data = []\n", + " \n", + " for p in tqdm(glob('/opt/iui-datarelease3-sose2021/*.csv')):\n", + " path = p\n", + " filename = path.split('/')[-1]\n", + " user = int(filename.split('_')[0][1:])\n", + " if (user_filter):\n", + " if (user != user_filter):\n", + " continue\n", + " scenario = filename.split('_')[1][len('Scenario'):]\n", + " heightnorm = filename.split('_')[2][len('HeightNormalization'):] == 'True'\n", + " armnorm = filename.split('_')[3][len('ArmNormalization'):] == 'True'\n", + " rep = int(filename.split('.')[0].split('_')[4][len('Repetition'):])\n", + " session = filename.split('_')[5][len('Session'):]\n", + " session = session.split('.')[0]\n", + " \n", + " data = pd.read_csv(path)\n", + " dic_data.append(\n", + " {\n", + " 'filename': path,\n", + " 'user': user,\n", + " 'scenario': scenario,\n", + " 'heightnorm': heightnorm,\n", + " 'armnorm': armnorm,\n", + " 'rep': rep,\n", + " 'session': session,\n", + " 'data': data \n", + " \n", + " }\n", + " )\n", + " return dic_data\n", + "\n", + "dic_data = load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3df066af", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# dataP = pd.DataFrame.from_dict(fil_dic_data) #pandas Dataframe Form mit 'data0' nur die daten\n", + "\n", + "# tempP = dataP['data']\n", + "\n", + "# tempP = tempP[0].drop(columns=['Scenario','HeightNormalization','ArmNormalization','LeftHandTrackingAccuracy','RightHandTrackingAccuracy']) #P without String Data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "30296cad", + "metadata": {}, + "outputs": [], + "source": [ + "fil_dic_data = []\n", + "for d in dic_data:\n", + " if d['scenario'] == 'Sorting':\n", + " if d['heightnorm'] == d['armnorm']:\n", + " fil_dic_data.append(d)" + ] + }, + { + "cell_type": "markdown", + "id": "7a808f50", + "metadata": {}, + "source": [ + "Test\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dc206ded", + "metadata": {}, + "outputs": [], + "source": [ + "min_Max = MinMaxScaler()\n", + "standard = StandardScaler()\n", + "max_Abs = MaxAbsScaler()\n", + "binarizer = Binarizer()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cf5b5695", + "metadata": {}, + "outputs": [], + "source": [ + "session_data_1 = []\n", + "session_data_2 = []\n", + "\n", + "user_data_1 = []\n", + "user_data_2 = []\n", + "\n", + "data_1 = []\n", + "data_2 = []\n", + "\n", + "for a in fil_dic_data:\n", + " if(a['session'] == '1'): ## Daten aus session 1 für train\n", + " session_data_1.append(a)\n", + " \n", + " if(a['session'] == '2'): ## Daten aus Session 2 zum validaten\n", + " session_data_2.append(a)\n", + "\n", + "for b in session_data_1:\n", + " user_data_1.append(b['user']) ## Label zu 1 \n", + " data_1.append(a['data'])\n", + " \n", + "for c in session_data_2:\n", + " user_data_2.append(b['user']) ## Label zu 2\n", + " data_2.append(a['data'])\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "82465bca", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:00<00:00, 575.65it/s]\n", + "100%|██████████| 96/96 [00:00<00:00, 646.85it/s]\n" + ] + } + ], + "source": [ + "dataF_1 = [] ## Filtered Data session 1\n", + "dataF_2 = [] ## Filtered Data session 2\n", + "\n", + "temp_1 = [] ## Temp Holder für 1\n", + "temp_2 = [] ## Temp Holder für 2\n", + "\n", + "counter = 0 ## Counter für Einspeisen der Daten\n", + "\n", + "\n", + "for a in data_1:\n", + " temp_1.append(pd.DataFrame(a))\n", + "\n", + "for b in data_2:\n", + " temp_2.append(pd.DataFrame(b))\n", + "\n", + "\n", + "for c in tqdm(temp_1):\n", + " dataF_1.append(c.drop(columns=['Scenario','HeightNormalization','ArmNormalization','LeftHandTrackingAccuracy','RightHandTrackingAccuracy','Unnamed: 0', 'FrameID','participantID','Repetition']))\n", + " counter +=1\n", + " \n", + "counter = 0\n", + "\n", + "for d in tqdm(temp_2):\n", + " dataF_2.append(c.drop(columns=['Scenario','HeightNormalization','ArmNormalization','LeftHandTrackingAccuracy','RightHandTrackingAccuracy','Unnamed: 0', 'FrameID','participantID','Repetition']))\n", + " counter +=1\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a6f7076e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 96/96 [00:03<00:00, 25.87it/s]\n" + ] + } + ], + "source": [ + "minD = [] ## normalisierte Daten durch Minmax\n", + "staD = [] ## normalisierte Daten durch Standard\n", + "maxD = [] ## normalisierte Daten durch MaxAbs\n", + "binD = [] ## normalisierte Daten durch binarizer\n", + "\n", + "for i in tqdm(dataF_1):\n", + " minD.append( min_Max.fit_transform(i))\n", + " staD.append(standard.fit_transform(i))\n", + " maxD.append(max_Abs.fit_transform(i))\n", + " binD.append(binarizer.fit_transform(i))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "94676652", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "x_train,x_test,y_train,y_test = train_test_split(minD,user_data_1,random_state=2)" + ] + }, + { + "cell_type": "markdown", + "id": "14a4abe1", + "metadata": {}, + "source": [ + "Classi" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": 5 +} diff --git a/2-second-project/tdt/DataViz.ipynb b/2-second-project/tdt/DataViz.ipynb index 63aa556..93c2ec2 100644 --- a/2-second-project/tdt/DataViz.ipynb +++ b/2-second-project/tdt/DataViz.ipynb @@ -2,10 +2,23 @@ "cells": [ { "cell_type": "code", +<<<<<<< HEAD "execution_count": 1, "id": "a33b4ae2", +======= + "execution_count": null, + "id": "39df48da", +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 59%|█████▉ | 453/768 [00:40<00:23, 13.66it/s]" + ] + } + ], "source": [ "import os\n", "\n", @@ -42,7 +55,11 @@ " \n", " dic_data = []\n", " \n", +<<<<<<< HEAD " for p in tqdm(glob(glob_path)):\n", +======= + " for p in tqdm(glob('/opt/iui-datarelease3-sose2021/*.csv')):\n", +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 " path = p\n", " filename = path.split('/')[-1].split('.')[0]\n", " splitname = filename.split('_')\n", @@ -50,11 +67,21 @@ " if (user_filter):\n", " if (user != user_filter):\n", " continue\n", +<<<<<<< HEAD " scenario = splitname[1][len('Scenario'):]\n", " heightnorm = splitname[2][len('HeightNormalization'):] == 'True'\n", " armnorm = splitname[3][len('ArmNormalization'):] == 'True'\n", " rep = int(splitname[4][len('Repetition'):])\n", " session = int(splitname[5][len('Session'):])\n", +======= + " scenario = filename.split('_')[1][len('Scenario'):]\n", + " heightnorm = filename.split('_')[2][len('HeightNormalization'):] == 'True'\n", + " armnorm = filename.split('_')[3][len('ArmNormalization'):] == 'True'\n", + " rep = int(filename.split('.')[0].split('_')[4][len('Repetition'):])\n", + " session = filename.split('_')[5][len('Session'):]\n", + " session = session.split('.')[0]\n", + " \n", +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 " data = pd.read_csv(path)\n", " dic_data.append(\n", " {\n", @@ -66,6 +93,7 @@ " 'rep': rep,\n", " 'session': session,\n", " 'data': data \n", + " \n", " }\n", " )\n", " return dic_data" @@ -1052,6 +1080,7 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 95, "id": "97c3ba71", "metadata": { @@ -1514,6 +1543,10 @@ "cell_type": "code", "execution_count": 96, "id": "7ab1aa62", +======= + "execution_count": 11, + "id": "855aa409", +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 "metadata": {}, "outputs": [ { @@ -1526,10 +1559,17 @@ { "data": { "text/plain": [ +<<<<<<< HEAD "" ] }, "execution_count": 96, +======= + "768" + ] + }, + "execution_count": 11, +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 "metadata": {}, "output_type": "execute_result" } @@ -1563,8 +1603,135 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 97, "id": "8827bbab", +======= + "execution_count": 12, + "id": "e1d660ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'filename': '/opt/iui-datarelease3-sose2021/P2_ScenarioSorting_HeightNormalizationFalse_ArmNormalizationTrue_Repetition0_Session1.csv', 'user': 2, 'scenario': 'Sorting', 'heightnorm': False, 'armnorm': True, 'rep': 0, 'session': ['1', 'csv'], 'data': Unnamed: 0 FrameID participantID Scenario \\\n", + "0 0 0 2 SortingBlocksScene \n", + "1 1 1 2 SortingBlocksScene \n", + "2 2 2 2 SortingBlocksScene \n", + "3 3 3 2 SortingBlocksScene \n", + "4 4 4 2 SortingBlocksScene \n", + "... ... ... ... ... \n", + "1734 1734 1734 2 SortingBlocksScene \n", + "1735 1735 1735 2 SortingBlocksScene \n", + "1736 1736 1736 2 SortingBlocksScene \n", + "1737 1737 1737 2 SortingBlocksScene \n", + "1738 1738 1738 2 SortingBlocksScene \n", + "\n", + " HeightNormalization ArmNormalization Repetition \\\n", + "0 False True 0 \n", + "1 False True 0 \n", + "2 False True 0 \n", + "3 False True 0 \n", + "4 False True 0 \n", + "... ... ... ... \n", + "1734 False True 0 \n", + "1735 False True 0 \n", + "1736 False True 0 \n", + "1737 False True 0 \n", + "1738 False True 0 \n", + "\n", + " LeftHandTrackingAccuracy CenterEyeAnchor_pos_X CenterEyeAnchor_pos_Y \\\n", + "0 High 0.092306 1.541967 \n", + "1 High 0.092339 1.542134 \n", + "2 High 0.092368 1.542324 \n", + "3 High 0.092528 1.542059 \n", + "4 High 0.092597 1.541883 \n", + "... ... ... ... \n", + "1734 Low -0.118714 1.209641 \n", + "1735 Low -0.120236 1.208805 \n", + "1736 Low -0.121484 1.208166 \n", + "1737 Low -0.122358 1.207728 \n", + "1738 Low -0.123330 1.207573 \n", + "\n", + " ... right_Hand_RingTip_euler_Y right_Hand_RingTip_euler_Z \\\n", + "0 ... 22.05669 133.1912 \n", + "1 ... 22.27071 132.8817 \n", + "2 ... 22.46026 132.6752 \n", + "3 ... 22.69390 132.5095 \n", + "4 ... 22.71980 132.6141 \n", + "... ... ... ... \n", + "1734 ... 276.90730 109.5109 \n", + "1735 ... 276.74050 110.6240 \n", + "1736 ... 276.70190 111.3780 \n", + "1737 ... 275.85200 112.9337 \n", + "1738 ... 275.01000 114.0286 \n", + "\n", + " right_Hand_PinkyTip_pos_X right_Hand_PinkyTip_pos_Y \\\n", + "0 0.153212 1.137668 \n", + "1 0.144130 1.161759 \n", + "2 0.144180 1.161877 \n", + "3 0.144442 1.161859 \n", + "4 0.144659 1.161498 \n", + "... ... ... \n", + "1734 0.079172 0.720395 \n", + "1735 0.076673 0.720138 \n", + "1736 0.075629 0.720296 \n", + "1737 0.076548 0.716786 \n", + "1738 0.078380 0.713724 \n", + "\n", + " right_Hand_PinkyTip_pos_Z right_Hand_PinkyTip_euler_X \\\n", + "0 0.254763 319.9573 \n", + "1 0.240258 320.2673 \n", + "2 0.240361 320.5427 \n", + "3 0.240253 320.9518 \n", + "4 0.239949 321.0735 \n", + "... ... ... \n", + "1734 1.235342 350.7823 \n", + "1735 1.245831 351.2207 \n", + "1736 1.252426 351.6184 \n", + "1737 1.262976 352.0619 \n", + "1738 1.269815 352.2750 \n", + "\n", + " right_Hand_PinkyTip_euler_Y right_Hand_PinkyTip_euler_Z Session \\\n", + "0 23.96579 143.5809 1 \n", + "1 23.99540 143.5675 1 \n", + "2 24.01744 143.6467 1 \n", + "3 23.98431 144.0452 1 \n", + "4 23.90498 144.4992 1 \n", + "... ... ... ... \n", + "1734 284.40790 113.3901 1 \n", + "1735 284.28250 114.6937 1 \n", + "1736 284.30020 115.6711 1 \n", + "1737 283.35190 117.7055 1 \n", + "1738 282.37350 119.1508 1 \n", + "\n", + " RightHandTrackingAccuracy \n", + "0 High \n", + "1 High \n", + "2 High \n", + "3 High \n", + "4 High \n", + "... ... \n", + "1734 Low \n", + "1735 Low \n", + "1736 Low \n", + "1737 Low \n", + "1738 Low \n", + "\n", + "[1739 rows x 346 columns]}\n" + ] + } + ], + "source": [ + "print (dic_data[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6284add1", +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 "metadata": {}, "outputs": [ { @@ -1690,24 +1857,37 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 126, "id": "bcec87a9", +======= + "execution_count": 5, + "id": "82167504", +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ +<<<<<<< HEAD "/opt/iui-datarelease3-sose2021/P14_ScenarioSorting_HeightNormalizationTrue_ArmNormalizationTrue_Repetition2.csv\n" +======= + "/opt/iui-datarelease3-sose2021/P7_ScenarioSorting_HeightNormalizationFalse_ArmNormalizationFalse_Repetition0_Session1.csv\n" +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 ] }, { "data": { "text/plain": [ - "337" + "336" ] }, +<<<<<<< HEAD "execution_count": 126, +======= + "execution_count": 5, +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 "metadata": {}, "output_type": "execute_result" } @@ -1725,8 +1905,13 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 98, "id": "fb10decc", +======= + "execution_count": 6, + "id": "badc87d6", +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 "metadata": {}, "outputs": [], "source": [ @@ -1737,33 +1922,38 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 110, "id": "08ae9f52", +======= + "execution_count": 7, + "id": "97c17107", +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "count 384.000000\n", - "mean 3053.768229\n", - "std 2195.831831\n", - "min 597.000000\n", - "50% 2395.000000\n", - "90% 5977.000000\n", - "91% 6157.600000\n", - "92% 6239.600000\n", - "93% 6341.490000\n", - "94% 6585.200000\n", - "95% 7561.800000\n", - "96% 8158.000000\n", - "97% 8895.250000\n", - "98% 9942.320000\n", - "99% 10315.120000\n", - "max 19371.000000\n", + "count 768.000000\n", + "mean 2606.889323\n", + "std 1941.835990\n", + "min 407.000000\n", + "50% 2102.000000\n", + "90% 4427.900000\n", + "91% 4606.940000\n", + "92% 4788.760000\n", + "93% 5131.990000\n", + "94% 5815.100000\n", + "95% 6182.450000\n", + "96% 6400.920000\n", + "97% 7223.400000\n", + "98% 8273.100000\n", + "99% 10162.200000\n", + "max 21108.000000\n", "dtype: float64" ] }, - "execution_count": 110, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1775,23 +1965,28 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 111, "id": "57fee43e", +======= + "execution_count": 8, + "id": "ab1295b4", +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 111, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1815,6 +2010,7 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 12, "id": "69bd85a5", "metadata": {}, @@ -1835,8 +2031,24 @@ "cell_type": "code", "execution_count": null, "id": "522518bc", +======= + "execution_count": 9, + "id": "b530d28c", +>>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'float64' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'float64' is not defined" + ] + } + ], "source": [ "pplot(x['data'][2:12])" ]