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ot.datasets — POT Python Optimal Transport 0.9.5 documentation

Simple example datasets

ot.datasets.make_1D_gauss(n, m, s)[source]

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 – 1D histogram for a gaussian distribution

Return type:

ndarray (n,)

ot.datasets.make_2D_samples_gauss(n, m, sigma, random_state=None)[source]

Return n samples drawn from 2D gaussian \(\mathcal{N}(m, \sigma)\)

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 – n samples drawn from \(\mathcal{N}(m, \sigma)\).

Return type:

ndarray, shape (n, 2)

ot.datasets.make_data_classif(dataset, n, nz=0.5, theta=0, p=0.5, random_state=None, **kwargs)[source]

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.

ot.datasets.make_1D_gauss(n, m, s)[source]

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 – 1D histogram for a gaussian distribution

Return type:

ndarray (n,)

ot.datasets.make_2D_samples_gauss(n, m, sigma, random_state=None)[source]

Return n samples drawn from 2D gaussian \(\mathcal{N}(m, \sigma)\)

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 – n samples drawn from \(\mathcal{N}(m, \sigma)\).

Return type:

ndarray, shape (n, 2)

ot.datasets.make_data_classif(dataset, n, nz=0.5, theta=0, p=0.5, random_state=None, **kwargs)[source]

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.

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