diff options
Diffstat (limited to 'ot/datasets.py')
| -rw-r--r-- | ot/datasets.py | 32 |
1 files changed, 12 insertions, 20 deletions
diff --git a/ot/datasets.py b/ot/datasets.py index e76e75d..ba0cfd9 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -17,7 +17,6 @@ def make_1D_gauss(n, m, s): Parameters ---------- - n : int number of bins in the histogram m : float @@ -25,12 +24,10 @@ def make_1D_gauss(n, m, s): s : float standard deviaton of the gaussian distribution - Returns ------- - h : np.array (n,) - 1D histogram for a gaussian distribution - + 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)) @@ -44,16 +41,15 @@ def get_1D_gauss(n, m, sigma): def make_2D_samples_gauss(n, m, sigma, random_state=None): - """return n samples drawn from 2D gaussian N(m,sigma) + """Return n samples drawn from 2D gaussian N(m,sigma) Parameters ---------- - n : int number of samples to make - m : np.array (2,) + m : ndarray, shape (2,) mean value of the gaussian distribution - sigma : np.array (2,2) + 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; @@ -63,9 +59,8 @@ def make_2D_samples_gauss(n, m, sigma, random_state=None): Returns ------- - X : np.array (n,2) - n samples drawn from N(m,sigma) - + X : ndarray, shape (n, 2) + n samples drawn from N(m, sigma). """ generator = check_random_state(random_state) @@ -86,11 +81,10 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None): def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): - """ dataset generation for classification problems + """Dataset generation for classification problems Parameters ---------- - dataset : str type of classification problem (see code) n : int @@ -105,13 +99,11 @@ def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): Returns ------- - X : np.array (n,d) - n observation of size d - y : np.array (n,) - labels of the samples - + 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': |