sklearn.utils.as_float_array — scikit-learn 0.20.4 documentation (original) (raw)

sklearn.utils. as_float_array(X, copy=True, force_all_finite=True)[source]¶

Converts an array-like to an array of floats.

The new dtype will be np.float32 or np.float64, depending on the original type. The function can create a copy or modify the argument depending on the argument copy.

Parameters:	X : {array-like, sparse matrix} copy : bool, optional If True, a copy of X will be created. If False, a copy may still be returned if X’s dtype is not a floating point type. force_all_finite : boolean or ‘allow-nan’, (default=True) Whether to raise an error on np.inf and np.nan in X. The possibilities are: True: Force all values of X to be finite. False: accept both np.inf and np.nan in X. ‘allow-nan’: accept only np.nan values in X. Values cannot be infinite. New in version 0.20: force_all_finite accepts the string 'allow-nan'.
Returns:	XT : {array, sparse matrix} An array of type np.float

Parameters:

X : {array-like, sparse matrix} copy : bool, optional If True, a copy of X will be created. If False, a copy may still be returned if X’s dtype is not a floating point type. force_all_finite : boolean or ‘allow-nan’, (default=True) Whether to raise an error on np.inf and np.nan in X. The possibilities are: True: Force all values of X to be finite. False: accept both np.inf and np.nan in X. ‘allow-nan’: accept only np.nan values in X. Values cannot be infinite. New in version 0.20: force_all_finite accepts the string 'allow-nan'.

Returns:

XT : {array, sparse matrix} An array of type np.float