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120 lines
2.2 KiB
120 lines
2.2 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Image Browser"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This example shows how to browse through a set of images with a slider."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from IPython.html.widgets import interact"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from sklearn import datasets"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We will use the digits dataset from [scikit-learn](http://scikit-learn.org/stable/)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"digits = datasets.load_digits()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def browse_images(digits):\n",
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" n = len(digits.images)\n",
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" def view_image(i):\n",
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" plt.imshow(digits.images[i], cmap=plt.cm.gray_r, interpolation='nearest')\n",
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" plt.title('Training: %s' % digits.target[i])\n",
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" plt.show()\n",
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" interact(view_image, i=(0,n-1))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"browse_images(digits)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.4.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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