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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,18 @@ | ||
| import sys | ||
| sys.path.append('..') | ||
|
|
||
| import numpy as np | ||
| from vidar import interactive_kmeans | ||
|
|
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| rng = np.random.RandomState(0) | ||
| n_samples = 1000 | ||
| cov = [[0.4, 0], [0, 0.4]] | ||
| X = np.concatenate([ | ||
| rng.multivariate_normal(mean=[-2, 0], cov=cov, size=n_samples), | ||
| rng.multivariate_normal(mean=[2, 0], cov=cov, size=n_samples), | ||
| rng.multivariate_normal(mean=[0.3, 1], cov=cov, size=n_samples) | ||
| ]) | ||
|
|
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| n_clusters = 2 | ||
| app = interactive_kmeans(X, n_clusters) | ||
| app.show() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,17 @@ | ||
| import sys | ||
| sys.path.append('..') | ||
|
|
||
| import numpy as np | ||
| from vidar import interactive_PCA | ||
|
|
||
| rng = np.random.RandomState(0) | ||
| n_samples = 1000 | ||
| cov = [[1, 0], [0, 1]] | ||
| X = np.concatenate([ | ||
| rng.multivariate_normal(mean=[-2, 0], cov=cov, size=n_samples), | ||
| rng.multivariate_normal(mean=[2, 0], cov=cov, size=n_samples)]) | ||
|
|
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|
|
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| n_components = 1 | ||
| app = interactive_PCA(X, n_components) | ||
| app.show() |
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notebooks/yang/Kmeans interactive _ more clusters.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,130 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# K-means interactive\n", | ||
| "\n", | ||
| "> Yang" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%matplotlib qt\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import numpy as np\n", | ||
| "from matplotlib.widgets import Button\n", | ||
| "from matplotlib.widgets import PolygonSelector\n", | ||
| "from sklearn.cluster import KMeans\n", | ||
| "\n", | ||
| "def colors_from_lbs(lbs, colors=None):\n", | ||
| " mpl_20 = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n", | ||
| " '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n", | ||
| " '#3397dc', '#ff993e', '#3fca3f', '#df5152', '#a985ca',\n", | ||
| " '#ad7165', '#e992ce', '#999999', '#dbdc3c', '#35d8e9']\n", | ||
| " \n", | ||
| " if colors is None:\n", | ||
| " colors = np.array(mpl_20)\n", | ||
| " else:\n", | ||
| " colors = np.array(colors)\n", | ||
| " lbs = np.array(lbs) % len(colors)\n", | ||
| " return colors[lbs]\n", | ||
| "\n", | ||
| "rng = np.random.RandomState(0)\n", | ||
| "n_samples = 1000\n", | ||
| "cov = [[0.4, 0], [0, 0.4]]\n", | ||
| "X = np.concatenate([\n", | ||
| " rng.multivariate_normal(mean=[-2, 0], cov=cov, size=n_samples), \n", | ||
| " rng.multivariate_normal(mean=[2, 0], cov=cov, size=n_samples),\n", | ||
| " rng.multivariate_normal(mean=[0.5, 1], cov=cov, size=n_samples)\n", | ||
| " ])\n", | ||
| "\n", | ||
| "n_clusters=3\n", | ||
| "kmeans = KMeans(n_clusters=n_clusters, random_state=0, n_init=\"auto\")\n", | ||
| "labels = kmeans.fit_predict(X)\n", | ||
| "\n", | ||
| "centers = kmeans.cluster_centers_\n", | ||
| "\n", | ||
| "fig, (ax_orig, ax_redim) = plt.subplots(1, 2, figsize=(12, 6))\n", | ||
| "\n", | ||
| "def plot_figure(axe_list, X, centers):\n", | ||
| " ax_orig, ax_redim = axe_list\n", | ||
| "\n", | ||
| " kmeans.cluster_centers_ = np.array(centers, dtype=np.float64)\n", | ||
| " labels = kmeans.predict(X) \n", | ||
| "\n", | ||
| " ax_orig.clear()\n", | ||
| " ax_orig.scatter(X[:, 0], X[:, 1], alpha=0.3, label=\"samples\", c=colors_from_lbs(labels))\n", | ||
| " ax_orig.scatter(centers[:,0], centers[:,1], s=50, c='black', edgecolors='r')\n", | ||
| " ax_orig.set(\n", | ||
| " aspect=\"auto\", \n", | ||
| " title=\"Interactive K-means\",\n", | ||
| " xlabel=\"first feature\",\n", | ||
| " ylabel=\"second feature\",\n", | ||
| " )\n", | ||
| "\n", | ||
| " ax_redim.clear()\n", | ||
| " class_name = ['class {0}'.format(i+1) for i in range(len(centers))]\n", | ||
| "\n", | ||
| " # update labels\n", | ||
| " counts = [np.sum(labels==i) for i in range(len(centers))]\n", | ||
| " \n", | ||
| "\n", | ||
| " ax_redim.bar(class_name, counts, label=class_name, color=colors_from_lbs(range(len(centers))))\n", | ||
| " ax_redim.set(\n", | ||
| " aspect=\"auto\",\n", | ||
| " title=\"Clustering results\",\n", | ||
| " xlabel=\"Main feature\",\n", | ||
| " ylabel=\"Number of samples\",\n", | ||
| " )\n", | ||
| " fig.canvas.draw_idle()\n", | ||
| "\n", | ||
| "plot_figure((ax_orig, ax_redim), X, centers)\n", | ||
| "\n", | ||
| "def onselect(verts):\n", | ||
| " centers = np.array(verts)\n", | ||
| " plot_figure((ax_orig, ax_redim), X, centers)\n", | ||
| "\n", | ||
| "selector = PolygonSelector(ax_orig, onselect=onselect, \n", | ||
| " props=dict(color='r', linestyle='', linewidth=3, alpha=0.6, label=f\"Component\"))\n", | ||
| "selector.verts = centers\n", | ||
| "\n", | ||
| "plt.tight_layout()\n", | ||
| "plt.show()\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "base", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.19" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,124 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# K-means interactive\n", | ||
| "\n", | ||
| "> Yang" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 16, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%matplotlib qt\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import numpy as np\n", | ||
| "from matplotlib.widgets import Button\n", | ||
| "from matplotlib.widgets import PolygonSelector\n", | ||
| "from sklearn.cluster import KMeans\n", | ||
| "\n", | ||
| "def colors_from_lbs(lbs, colors=None):\n", | ||
| " mpl_20 = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n", | ||
| " '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n", | ||
| " '#3397dc', '#ff993e', '#3fca3f', '#df5152', '#a985ca',\n", | ||
| " '#ad7165', '#e992ce', '#999999', '#dbdc3c', '#35d8e9']\n", | ||
| " \n", | ||
| " if colors is None:\n", | ||
| " colors = np.array(mpl_20)\n", | ||
| " else:\n", | ||
| " colors = np.array(colors)\n", | ||
| " lbs = np.array(lbs) % len(colors)\n", | ||
| " return colors[lbs]\n", | ||
| "\n", | ||
| "rng = np.random.RandomState(0)\n", | ||
| "n_samples = 1000\n", | ||
| "cov = [[0.4, 0], [0, 0.4]]\n", | ||
| "X = np.concatenate([\n", | ||
| " rng.multivariate_normal(mean=[-2, 0], cov=cov, size=n_samples), \n", | ||
| " rng.multivariate_normal(mean=[2, 0], cov=cov, size=n_samples),\n", | ||
| " rng.multivariate_normal(mean=[0.3, 1], cov=cov, size=n_samples)\n", | ||
| " ])\n", | ||
| "\n", | ||
| "kmeans = KMeans(n_clusters=2, random_state=0, n_init=\"auto\")\n", | ||
| "labels = kmeans.fit_predict(X)\n", | ||
| "\n", | ||
| "centers = kmeans.cluster_centers_\n", | ||
| "\n", | ||
| "fig, (ax_orig, ax_redim) = plt.subplots(1, 2, figsize=(12, 6))\n", | ||
| "\n", | ||
| "def plot_figure(axe_list, X, centers):\n", | ||
| " ax_orig, ax_redim = axe_list\n", | ||
| "\n", | ||
| " kmeans.cluster_centers_ = np.array(centers, dtype=np.float64)\n", | ||
| " labels = kmeans.predict(X) \n", | ||
| "\n", | ||
| " ax_orig.clear()\n", | ||
| " ax_orig.scatter(X[:, 0], X[:, 1], alpha=0.3, label=\"samples\", c=colors_from_lbs(labels))\n", | ||
| " ax_orig.scatter(centers[:,0], centers[:,1], s=50, c='black', edgecolors='r')\n", | ||
| " ax_orig.set(\n", | ||
| " aspect=\"auto\", \n", | ||
| " title=\"Interactive K-means\",\n", | ||
| " xlabel=\"first feature\",\n", | ||
| " ylabel=\"second feature\",\n", | ||
| " )\n", | ||
| "\n", | ||
| " ax_redim.clear()\n", | ||
| " class_name = ['class {0}'.format(i+1) for i in range(len(centers))]\n", | ||
| "\n", | ||
| " # update labels\n", | ||
| " counts = [np.sum(labels==i) for i in range(len(centers))]\n", | ||
| " \n", | ||
| "\n", | ||
| " ax_redim.bar(class_name, counts, \n", | ||
| " label=class_name,\n", | ||
| " color=colors_from_lbs(range(len(centers))))\n", | ||
| " ax_redim.set(\n", | ||
| " aspect=\"auto\",\n", | ||
| " title=\"Clustering results\",\n", | ||
| " xlabel=\"Main feature\",\n", | ||
| " ylabel=\"Number of samples\",\n", | ||
| " )\n", | ||
| " fig.canvas.draw_idle()\n", | ||
| "\n", | ||
| "plot_figure((ax_orig, ax_redim), X, centers)\n", | ||
| "\n", | ||
| "def onselect(verts):\n", | ||
| " centers = np.array(verts)\n", | ||
| " plot_figure((ax_orig, ax_redim), X, centers)\n", | ||
| "\n", | ||
| "selector = PolygonSelector(ax_orig, onselect=onselect, \n", | ||
| " props=dict(color='r', linestyle='', linewidth=3, alpha=0.6, label=f\"Component\"))\n", | ||
| "selector.verts = centers\n", | ||
| "\n", | ||
| "plt.tight_layout()\n", | ||
| "plt.show()\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "base", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.19" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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