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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Synthetic dataset generators."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.learn.python.learn.datasets.base import Dataset
def circles(n_samples=100, noise=None, seed=None, factor=0.8, n_classes=2, *args, **kwargs):
"""Create circles separated by some value
Args:
n_samples: int, number of datapoints to generate
noise: float or None, standard deviation of the Gaussian noise added
seed: int or None, seed for the noise
factor: float, size factor of the inner circles with respect to the outer ones
n_classes: int, number of classes to generate
Returns:
Shuffled features and labels for 'circles' synthetic dataset of type `base.Dataset`
Note:
The multi-class support might not work as expected if `noise` is enabled
TODO:
- Generation of unbalanced data
Credit goes to (under BSD 3 clause):
B. Thirion,
G. Varoquaux,
A. Gramfort,
V. Michel,
O. Grisel,
G. Louppe,
J. Nothman
"""
if seed is not None:
np.random.seed(seed)
# Algo: 1) Generate initial circle, 2) For ever class generate a smaller radius circle
linspace = np.linspace(0, 2*np.pi, n_samples // n_classes)
circ_x = np.empty(0, dtype=np.int32)
circ_y = np.empty(0, dtype=np.int32)
base_cos = np.cos(linspace)
base_sin = np.sin(linspace)
y = np.empty(0, dtype=np.int32)
for label in range(n_classes):
circ_x = np.append(circ_x, base_cos)
circ_y = np.append(circ_y, base_sin)
base_cos *= factor
base_sin *= factor
y = np.append(y, label*np.ones(n_samples // n_classes, dtype=np.int32))
# Add more points if n_samples is not divisible by n_classes (unbalanced!)
extras = n_samples % n_classes
circ_x = np.append(circ_x, np.cos(np.random.rand(extras)*2*np.pi))
circ_y = np.append(circ_y, np.sin(np.random.rand(extras)*2*np.pi))
y = np.append(y, np.zeros(extras, dtype=np.int32))
# Reshape the features/labels
X = np.vstack((circ_x, circ_y)).T
y = np.hstack(y)
# Shuffle the data
indices = np.random.permutation(range(n_samples))
if noise is not None:
X += np.random.normal(scale=noise, size=X.shape)
return Dataset(data=X[indices], target=y[indices])
def spirals(n_samples=100, noise=None, seed=None,
mode = 'archimedes',
n_loops = 2,
*args, **kwargs):
"""Create spirals
Currently only binary classification is supported for spiral generation
Args:
n_samples: int, number of datapoints to generate
noise: float or None, standard deviation of the Gaussian noise added
seed: int or None, seed for the noise
n_loops: int, number of spiral loops, doesn't play well with 'bernoulli'
mode: str, how the spiral should be generated. Current implementations:
'archimedes': a spiral with equal distances between branches
'bernoulli': logarithmic spiral with branch distances increasing
'fermat': a spiral with branch distances decreasing (sqrt)
Returns:
Shuffled features and labels for 'spirals' synthetic dataset of type `base.Dataset`
Raises:
ValueError: If the generation `mode` is not valid
TODO:
- Generation of unbalanced data
"""
n_classes = 2 # I am not sure how to make it multiclass
_modes = {
'archimedes': _archimedes_spiral,
'bernoulli': _bernoulli_spiral,
'fermat': _fermat_spiral
}
if mode is None or mode not in _modes:
raise ValueError("Cannot generate spiral with mode %s"%mode)
if seed is not None:
np.random.seed(seed)
linspace = np.linspace(0, 2*n_loops*np.pi, n_samples // n_classes)
spir_x = np.empty(0, dtype=np.int32)
spir_y = np.empty(0, dtype=np.int32)
y = np.empty(0, dtype=np.int32)
for label in range(n_classes):
base_cos, base_sin = _modes[mode](linspace, label*np.pi, *args, **kwargs)
spir_x = np.append(spir_x, base_cos)
spir_y = np.append(spir_y, base_sin)
y = np.append(y, label*np.ones(n_samples // n_classes, dtype=np.int32))
# Add more points if n_samples is not divisible by n_classes (unbalanced!)
extras = n_samples % n_classes
if extras > 0:
x_exrta, y_extra = _modes[mode](np.random.rand(extras)*2*np.pi, *args, **kwargs)
spir_x = np.append(spir_x, x_extra)
spir_y = np.append(spir_y, y_extra)
y = np.append(y, np.zeros(extras, dtype=np.int32))
# Reshape the features/labels
X = np.vstack((spir_x, spir_y)).T
y = np.hstack(y)
# Shuffle the data
indices = np.random.permutation(range(n_samples))
if noise is not None:
X += np.random.normal(scale=noise, size=X.shape)
return Dataset(data=X[indices], target=y[indices])
def _archimedes_spiral(theta, theta_offset=0., *args, **kwargs):
"""Return Archimedes spiral
Args:
theta: array-like, angles from polar coordinates to be converted
theta_offset: float, angle offset in radians (2*pi = 0)
"""
x, y = theta*np.cos(theta + theta_offset), theta*np.sin(theta + theta_offset)
x_norm = np.max(np.abs(x))
y_norm = np.max(np.abs(y))
x, y = x / x_norm, y / y_norm
return x, y
def _bernoulli_spiral(theta, theta_offset=0., *args, **kwargs):
"""Return Equiangular (Bernoulli's) spiral
Args:
theta: array-like, angles from polar coordinates to be converted
theta_offset: float, angle offset in radians (2*pi = 0)
Kwargs:
exp_scale: growth rate of the exponential
"""
exp_scale = kwargs.pop('exp_scale', 0.1)
x, y = np.exp(exp_scale*theta)*np.cos(theta + theta_offset), np.exp(exp_scale*theta)*np.sin(theta + theta_offset)
x_norm = np.max(np.abs(x))
y_norm = np.max(np.abs(y))
x, y = x / x_norm, y / y_norm
return x, y
def _fermat_spiral(theta, theta_offset=0., *args, **kwargs):
"""Return Parabolic (Fermat's) spiral
Args:
theta: array-like, angles from polar coordinates to be converted
theta_offset: float, angle offset in radians (2*pi = 0)
"""
x, y = np.sqrt(theta)*np.cos(theta + theta_offset), np.sqrt(theta)*np.sin(theta + theta_offset)
x_norm = np.max(np.abs(x))
y_norm = np.max(np.abs(y))
x, y = x / x_norm, y / y_norm
return x, y
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