"""
Simple example datasets
"""
# Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import numpy as np
import scipy as sp
from .utils import check_random_state, deprecated
[docs]
def make_1D_gauss(n, m, s):
"""return a 1D histogram for a gaussian distribution (`n` bins, mean `m` and std `s`)
Parameters
----------
n : int
number of bins in the histogram
m : float
mean value of the gaussian distribution
s : float
standard deviation of the gaussian distribution
Returns
-------
h : ndarray (`n`,)
1D histogram for a gaussian distribution
"""
x = np.arange(n, dtype=np.float64)
h = np.exp(-((x - m) ** 2) / (2 * s**2))
return h / h.sum()
@deprecated()
def get_1D_gauss(n, m, sigma):
"""Deprecated see make_1D_gauss"""
return make_1D_gauss(n, m, sigma)
[docs]
def make_2D_samples_gauss(n, m, sigma, random_state=None):
r"""Return `n` samples drawn from 2D gaussian :math:`\mathcal{N}(m, \sigma)`
Parameters
----------
n : int
number of samples to make
m : ndarray, shape (2,)
mean value of the gaussian distribution
sigma : ndarray, shape (2, 2)
covariance matrix of the gaussian distribution
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Returns
-------
X : ndarray, shape (`n`, 2)
n samples drawn from :math:`\mathcal{N}(m, \sigma)`.
"""
generator = check_random_state(random_state)
if np.isscalar(sigma):
sigma = np.array(
[
sigma,
]
)
if len(sigma) > 1:
P = sp.linalg.sqrtm(sigma)
res = generator.randn(n, 2).dot(P) + m
else:
res = generator.randn(n, 2) * np.sqrt(sigma) + m
return res
@deprecated()
def get_2D_samples_gauss(n, m, sigma, random_state=None):
"""Deprecated see make_2D_samples_gauss"""
return make_2D_samples_gauss(n, m, sigma, random_state=None)
[docs]
def make_data_classif(dataset, n, nz=0.5, theta=0, p=0.5, random_state=None, **kwargs):
"""Dataset generation for classification problems
Parameters
----------
dataset : str
type of classification problem (see code)
n : int
number of training samples
nz : float
noise level (>0)
p : float
proportion of one class in the binary setting
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Returns
-------
X : ndarray, shape (n, d)
`n` observation of size `d`
y : ndarray, shape (n,)
labels of the samples.
"""
generator = check_random_state(random_state)
if dataset.lower() == "3gauss":
y = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
x = np.zeros((n, 2))
# class 1
x[y == 1, 0] = -1.0
x[y == 1, 1] = -1.0
x[y == 2, 0] = -1.0
x[y == 2, 1] = 1.0
x[y == 3, 0] = 1.0
x[y == 3, 1] = 0
x[y != 3, :] += 1.5 * nz * generator.randn(sum(y != 3), 2)
x[y == 3, :] += 2 * nz * generator.randn(sum(y == 3), 2)
elif dataset.lower() == "3gauss2":
y = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
x = np.zeros((n, 2))
y[y == 4] = 3
# class 1
x[y == 1, 0] = -2.0
x[y == 1, 1] = -2.0
x[y == 2, 0] = -2.0
x[y == 2, 1] = 2.0
x[y == 3, 0] = 2.0
x[y == 3, 1] = 0
x[y != 3, :] += nz * generator.randn(sum(y != 3), 2)
x[y == 3, :] += 2 * nz * generator.randn(sum(y == 3), 2)
elif dataset.lower() == "gaussrot":
rot = np.array(
[[np.cos(theta), np.sin(theta)], [-np.sin(theta), np.cos(theta)]]
)
m1 = np.array([-1, 1])
m2 = np.array([1, -1])
y = np.floor((np.arange(n) * 1.0 / n * 2)) + 1
n1 = np.sum(y == 1)
n2 = np.sum(y == 2)
x = np.zeros((n, 2))
x[y == 1, :] = make_2D_samples_gauss(n1, m1, nz, random_state=generator)
x[y == 2, :] = make_2D_samples_gauss(n2, m2, nz, random_state=generator)
x = x.dot(rot)
elif dataset.lower() == "2gauss_prop":
y = np.concatenate((np.ones(int(p * n)), np.zeros(int((1 - p) * n))))
x = np.hstack((0 * y[:, None] - 0, 1 - 2 * y[:, None])) + nz * generator.randn(
len(y), 2
)
if ("bias" not in kwargs) and ("b" not in kwargs):
kwargs["bias"] = np.array([0, 2])
x[:, 0] += kwargs["bias"][0]
x[:, 1] += kwargs["bias"][1]
else:
x = np.array(0)
y = np.array(0)
print("unknown dataset")
return x, y.astype(int)
@deprecated()
def get_data_classif(dataset, n, nz=0.5, theta=0, random_state=None, **kwargs):
"""Deprecated see make_data_classif"""
return make_data_classif(dataset, n, nz=0.5, theta=0, random_state=None, **kwargs)
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