def fit(self, x, indices=None, axis=1):
"""Fitting parameter.
Args:
x (numpy.ndarray or cupy.ndarray or Variable):
indices (list or tuple or None):
indices for applying standard scaling.
axis (int): axis to calculate mean & std.
Returns:
self (StandardScaler): this instance.
"""
x = to_array(x)
x = format_x(x)
x = ShapeTransformerTo2D(axis=axis).transform(x).array
if indices is None:
pass
elif isinstance(indices, (list, tuple)):
indices = numpy.asarray(indices)
self.indices = indices
if self.indices is not None:
x = x[:, self.indices]
xp = self.xp
if xp is numpy:
x = cuda.to_cpu(x)
self.mean = xp.nanmean(x, axis=0)
self.std = xp.nanstd(x, axis=0)
else:
x = cuda.to_gpu(x)
if int(xp.sum(xp.isnan(x))) > 0:
raise NotImplementedError(
"StandardScaling with nan value on GPU is not supported.")
# cupy.nanmean, cupy.nanstd is not implemented yet.
self.mean = xp.mean(x, axis=0)
self.std = xp.std(x, axis=0)
# result consistency check
if xp.sum(self.std == 0) > 0:
logger = getLogger(__name__)
ind = numpy.argwhere(cuda.to_cpu(self.std) == 0)[:, 0]
logger.warning('fit: std was 0 at indices {}'.format(ind))
return self
def fit(self, x, indices=None):
"""Fitting parameter.
Args:
x:
indices (list or tuple or None):
indices for applying standard scaling.
Returns:
self (StandardScaler): this instance.
"""
x = to_array(x)
x = format_x(x)
if indices is None:
pass
elif isinstance(indices, (list, tuple)):
indices = numpy.asarray(indices)
self.indices = indices
if self.indices is not None:
x = x[:, self.indices]
xp = self.xp
if xp is numpy:
self.mean = xp.nanmean(x, axis=0)
self.std = xp.nanstd(x, axis=0)
else:
if int(xp.sum(xp.isnan(x))) > 0:
raise NotImplementedError(
"StandardScaling with nan value on GPU is not supported.")
# cupy.nanmean, cupy.nanstd is not implemented yet.
self.mean = xp.mean(x, axis=0)
self.std = xp.std(x, axis=0)
# result consistency check
if xp.sum(self.std == 0) > 0:
logger = getLogger(__name__)
ind = numpy.argwhere(cuda.to_cpu(self.std) == 0)[:, 0]
logger.warning('fit: std was 0 at indices {}'.format(ind))
return self
def fit(self, x, indices=None, axis=1):
"""Fitting parameter.
Args:
x (numpy.ndarray or cupy.ndarray or Variable):
indices (list or tuple or None):
indices for applying standard scaling.
axis (int): axis to calculate min & max.
Returns:
self (MinMaxScaler): this instance.
"""
x = to_array(x)
x = format_x(x)
x = ShapeTransformerTo2D(axis=axis).transform(x).array
if indices is None:
pass
elif isinstance(indices, (list, tuple)):
indices = numpy.asarray(indices)
self.indices = indices
if self.indices is not None:
x = x[:, self.indices]
xp = self.xp
if xp is numpy:
x = cuda.to_cpu(x)
else:
x = cuda.to_gpu(x)
self.min = xp.nanmin(x, axis=0)
self.max = xp.nanmax(x, axis=0)
# result consistency check
if xp.sum(self.max - self.min == 0) > 0:
logger = getLogger(__name__)
ind = numpy.argwhere(cuda.to_cpu(self.max-self.min) == 0)[:, 0]
logger.warning('fit: max-min was 0 at indices {}'.format(ind))
return self