def memorize_chunk(self, x, center=True, rescale=True, ddof=0):
x = atleast_2d_column_default(x)
if self.current_mean is None:
self.current_mean = np.zeros(x.shape[1], dtype=wide_dtype_for(x))
self.current_M2 = np.zeros(x.shape[1], dtype=wide_dtype_for(x))
# XX this can surely be vectorized but I am feeling lazy:
for i in xrange(x.shape[0]):
self.current_n += 1
delta = x[i, :] - self.current_mean
self.current_mean += delta / self.current_n
self.current_M2 += delta * (x[i, :] - self.current_mean)
def memorize_chunk(self, x, center=True, rescale=True, ddof=0):
x = atleast_2d_column_default(x)
if self.current_mean is None:
self.current_mean = np.zeros(x.shape[1], dtype=wide_dtype_for(x))
self.current_M2 = np.zeros(x.shape[1], dtype=wide_dtype_for(x))
# XX this can surely be vectorized but I am feeling lazy:
for i in range(x.shape[0]):
self.current_n += 1
delta = x[i, :] - self.current_mean
self.current_mean += delta / self.current_n
self.current_M2 += delta * (x[i, :] - self.current_mean)
def memorize_chunk(self, x):
x = atleast_2d_column_default(x)
self._count += x.shape[0]
this_total = np.sum(x, 0, dtype=wide_dtype_for(x))
# This is to handle potentially multi-column x's:
if self._sum is None:
self._sum = this_total
else:
self._sum += this_total
def memorize_chunk(self, x):
x = atleast_2d_column_default(x)
self._count += x.shape[0]
this_total = np.sum(x, 0, dtype=wide_dtype_for(x))
# This is to handle potentially multi-column x's:
if self._sum is None:
self._sum = this_total
else:
self._sum += this_total
