val count_nonzero :
?axis:[ `None | `PyObject of Py.Object.t ] ->
?sample_weight:Ndarray.t ->
x:Py.Object.t ->
unit ->
Py.Object.tA variant of X.getnnz() with extension to weighting on axis 0
Useful in efficiently calculating multilabel metrics.
Parameters ---------- X : CSR sparse matrix of shape (n_samples, n_labels) Input data.
axis : None, 0 or 1 The axis on which the data is aggregated.
sample_weight : array-like of shape (n_samples,), default=None Weight for each row of X.
val csc_median_axis_0 : x:Py.Object.t -> unit -> Ndarray.tFind the median across axis 0 of a CSC matrix. It is equivalent to doing np.median(X, axis=0).
Parameters ---------- X : CSC sparse matrix, shape (n_samples, n_features) Input data.
Returns ------- median : ndarray, shape (n_features,) Median.
val incr_mean_variance_axis :
x:Py.Object.t ->
axis:Py.Object.t ->
last_mean:Py.Object.t ->
last_var:Py.Object.t ->
last_n:Py.Object.t ->
unit ->
Py.Object.t * Py.Object.t * Py.Object.tCompute incremental mean and variance along an axix on a CSR or CSC matrix.
last_mean, last_var are the statistics computed at the last step by this function. Both must be initialized to 0-arrays of the proper size, i.e. the number of features in X. last_n is the number of samples encountered until now.
Parameters ---------- X : CSR or CSC sparse matrix, shape (n_samples, n_features) Input data.
axis : int (either 0 or 1) Axis along which the axis should be computed.
last_mean : float array with shape (n_features,) Array of feature-wise means to update with the new data X.
last_var : float array with shape (n_features,) Array of feature-wise var to update with the new data X.
last_n : int with shape (n_features,) Number of samples seen so far, excluded X.
Returns -------
means : float array with shape (n_features,) Updated feature-wise means.
variances : float array with shape (n_features,) Updated feature-wise variances.
n : int with shape (n_features,) Updated number of seen samples.
Notes ----- NaNs are ignored in the algorithm.
val inplace_column_scale :
x:Py.Object.t ->
scale:Py.Object.t ->
unit ->
Py.Object.tInplace column scaling of a CSC/CSR matrix.
Scale each feature of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape.
Parameters ---------- X : CSC or CSR matrix with shape (n_samples, n_features) Matrix to normalize using the variance of the features.
scale : float array with shape (n_features,) Array of precomputed feature-wise values to use for scaling.
val inplace_csr_column_scale :
x:Py.Object.t ->
scale:Py.Object.t ->
unit ->
Py.Object.tInplace column scaling of a CSR matrix.
Scale each feature of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape.
Parameters ---------- X : CSR matrix with shape (n_samples, n_features) Matrix to normalize using the variance of the features.
scale : float array with shape (n_features,) Array of precomputed feature-wise values to use for scaling.
val inplace_csr_row_scale :
x:Py.Object.t ->
scale:Py.Object.t ->
unit ->
Py.Object.tInplace row scaling of a CSR matrix.
Scale each sample of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape.
Parameters ---------- X : CSR sparse matrix, shape (n_samples, n_features) Matrix to be scaled.
scale : float array with shape (n_samples,) Array of precomputed sample-wise values to use for scaling.
val inplace_row_scale :
x:Py.Object.t ->
scale:Py.Object.t ->
unit ->
Py.Object.tInplace row scaling of a CSR or CSC matrix.
Scale each row of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape.
Parameters ---------- X : CSR or CSC sparse matrix, shape (n_samples, n_features) Matrix to be scaled.
scale : float array with shape (n_features,) Array of precomputed sample-wise values to use for scaling.
val inplace_swap_column :
x:Py.Object.t ->
m:int ->
n:int ->
unit ->
Py.Object.tSwaps two columns of a CSC/CSR matrix in-place.
Parameters ---------- X : CSR or CSC sparse matrix, shape=(n_samples, n_features) Matrix whose two columns are to be swapped.
m : int Index of the column of X to be swapped.
n : int Index of the column of X to be swapped.
val inplace_swap_row : x:Py.Object.t -> m:int -> n:int -> unit -> Py.Object.tSwaps two rows of a CSC/CSR matrix in-place.
Parameters ---------- X : CSR or CSC sparse matrix, shape=(n_samples, n_features) Matrix whose two rows are to be swapped.
m : int Index of the row of X to be swapped.
n : int Index of the row of X to be swapped.
val inplace_swap_row_csc :
x:Py.Object.t ->
m:int ->
n:int ->
unit ->
Py.Object.tSwaps two rows of a CSC matrix in-place.
Parameters ---------- X : scipy.sparse.csc_matrix, shape=(n_samples, n_features) Matrix whose two rows are to be swapped.
m : int Index of the row of X to be swapped.
n : int Index of the row of X to be swapped.
val inplace_swap_row_csr :
x:Py.Object.t ->
m:int ->
n:int ->
unit ->
Py.Object.tSwaps two rows of a CSR matrix in-place.
Parameters ---------- X : scipy.sparse.csr_matrix, shape=(n_samples, n_features) Matrix whose two rows are to be swapped.
m : int Index of the row of X to be swapped.
n : int Index of the row of X to be swapped.
val mean_variance_axis :
x:Py.Object.t ->
axis:Py.Object.t ->
unit ->
Py.Object.t * Py.Object.tCompute mean and variance along an axix on a CSR or CSC matrix
Parameters ---------- X : CSR or CSC sparse matrix, shape (n_samples, n_features) Input data.
axis : int (either 0 or 1) Axis along which the axis should be computed.
Returns -------
means : float array with shape (n_features,) Feature-wise means
variances : float array with shape (n_features,) Feature-wise variances
val min_max_axis :
?ignore_nan:bool ->
x:Py.Object.t ->
axis:Py.Object.t ->
unit ->
Py.Object.t * Py.Object.tCompute minimum and maximum along an axis on a CSR or CSC matrix and optionally ignore NaN values.
Parameters ---------- X : CSR or CSC sparse matrix, shape (n_samples, n_features) Input data.
axis : int (either 0 or 1) Axis along which the axis should be computed.
ignore_nan : bool, default is False Ignore or passing through NaN values.
.. versionadded:: 0.20
Returns -------
mins : float array with shape (n_features,) Feature-wise minima
maxs : float array with shape (n_features,) Feature-wise maxima