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# -*- coding: utf-8 -*- |
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import numpy |
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from collections import OrderedDict |
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from fuel import config |
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from fuel.datasets import IndexableDataset |
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class Spiral(IndexableDataset): |
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u"""Toy dataset containing points sampled from spirals on a 2d plane. |
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The dataset contains 3 sources: |
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* features -- the (x, y) position of the datapoints |
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* position -- the relative position on the spiral arm |
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* label -- the class labels (spiral arm) |
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.. plot:: |
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from fuel.datasets.toy import Spiral |
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ds = Spiral(classes=3) |
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features, position, label = ds.get_data(None, slice(0, 500)) |
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plt.title("Datapoints drawn from Spiral(classes=3)") |
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for l, m in enumerate(['o', '^', 'v']): |
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mask = label == l |
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plt.scatter(features[mask,0], features[mask,1], |
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c=position[mask], marker=m, label="label==%d"%l) |
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plt.xlim(-1.2, 1.2) |
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plt.ylim(-1.2, 1.2) |
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plt.legend() |
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plt.colorbar() |
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plt.xlabel("features[:,0]") |
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plt.ylabel("features[:,1]") |
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plt.show() |
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Parameters |
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---------- |
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num_examples : int |
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Number of datapoints to create. |
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classes : int |
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Number of spiral arms. |
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cycles : float |
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Number of turns the arms take. |
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noise : float |
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Add normal distributed noise with standard deviation *noise*. |
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""" |
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def __init__(self, num_examples=1000, classes=1, cycles=1., noise=0.0, |
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**kwargs): |
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seed = kwargs.pop('seed', config.default_seed) |
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rng = numpy.random.RandomState(seed) |
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# Create dataset |
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pos = rng.uniform(size=num_examples, low=0, high=cycles) |
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label = rng.randint(size=num_examples, low=0, high=classes) |
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radius = (2 * pos + 1) / 3. |
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phase_offset = label * (2*numpy.pi) / classes |
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features = numpy.zeros(shape=(num_examples, 2), dtype='float32') |
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features[:, 0] = radius * numpy.sin(2*numpy.pi*pos + phase_offset) |
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features[:, 1] = radius * numpy.cos(2*numpy.pi*pos + phase_offset) |
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features += noise * rng.normal(size=(num_examples, 2)) |
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data = OrderedDict([ |
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('features', features), |
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('position', pos), |
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('label', label), |
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]) |
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super(Spiral, self).__init__(data, **kwargs) |
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class SwissRoll(IndexableDataset): |
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"""Dataset containing points from a 3-dimensional Swiss roll. |
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The dataset contains 2 sources: |
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* features -- the x, y and z position of the datapoints |
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* position -- radial and z position on the manifold |
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.. plot:: |
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from fuel.datasets.toy import SwissRoll |
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import mpl_toolkits.mplot3d.axes3d as p3 |
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import numpy as np |
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ds = SwissRoll() |
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features, pos = ds.get_data(None, slice(0, 1000)) |
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color = pos[:,0] |
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color -= color.min() |
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color /= color.max() |
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fig = plt.figure() |
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ax = fig.gca(projection="3d") |
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ax.scatter(features[:,0], features[:,1], features[:,2], |
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'x', c=color) |
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ax.set_xlim(-1, 1) |
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ax.set_ylim(-1, 1) |
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ax.set_zlim(-1, 1) |
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ax.view_init(10., 10.) |
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plt.show() |
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Parameters |
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---------- |
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num_examples : int |
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Number of datapoints to create. |
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noise : float |
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Add normal distributed noise with standard deviation *noise*. |
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""" |
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def __init__(self, num_examples=1000, noise=0.0, **kwargs): |
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cycles = 1.5 |
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seed = kwargs.pop('seed', config.default_seed) |
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rng = numpy.random.RandomState(seed) |
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pos = rng.uniform(size=num_examples, low=0, high=1) |
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phi = cycles * numpy.pi * (1 + 2*pos) |
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radius = (1 + 2 * pos) / 3 |
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x = radius * numpy.cos(phi) |
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y = radius * numpy.sin(phi) |
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z = rng.uniform(size=num_examples, low=-1, high=1) |
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features = numpy.zeros(shape=(num_examples, 3), dtype='float32') |
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features[:, 0] = x |
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features[:, 1] = y |
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features[:, 2] = z |
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features += noise * rng.normal(size=(num_examples, 3)) |
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position = numpy.zeros(shape=(num_examples, 2), dtype='float32') |
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position[:, 0] = pos |
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position[:, 1] = z |
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data = OrderedDict([ |
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('features', features), |
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('position', position), |
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]) |
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super(SwissRoll, self).__init__(data, **kwargs) |
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mila-udem /
fuel
