def forward_val(self, x_, point=None):
x = x_.T
# reverse cumsum
x0 = x[:-1]
s = np.cumsum(x0[::-1], 0)[::-1] + x[-1]
z = x0 / s
Km1 = x.shape[0] - 1
k = np.arange(Km1)[(slice(None), ) + (None, ) * (x.ndim - 1)]
eq_share = nplogit(1. / (Km1 + 1 - k).astype(str(x_.dtype)))
y = nplogit(z) - eq_share
return floatX(y.T)
def forward_val(self, x_, point=None):
x = x_.T
# reverse cumsum
x0 = x[:-1]
s = np.cumsum(x0[::-1], 0)[::-1] + x[-1]
z = x0 / s
Km1 = x.shape[0] - 1
k = np.arange(Km1)[(slice(None),) + (None,) * (x.ndim - 1)]
eq_share = nplogit(1. / (Km1 + 1 - k).astype(str(x_.dtype)))
y = nplogit(z) - eq_share
return floatX(y.T)
def backward_val(self, y_):
y = y_.T
Km1 = y.shape[0]
k = np.arange(Km1)[(slice(None), ) + (None, ) * (y.ndim - 1)]
eq_share = nplogit(1.0 / (Km1 + 1 - k).astype(str(y_.dtype)))
z = expit(y + eq_share)
yl = np.concatenate([z, np.ones(y[:1].shape)])
yu = np.concatenate([np.ones(y[:1].shape), 1 - z])
S = np.cumprod(yu, 0)
x = S * yl
return floatX(x.T)
def forward_val(self, x, point=None):
return nplogit(x)
def forward_val(self, x, point=None):
return nplogit(x)
