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now ising numpy for std_norm_cdf

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James Hensman 2014-09-23 08:54:50 +01:00
parent 3134f52d7d
commit be8fa21cbc
2 changed files with 33 additions and 32 deletions
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1
GPy/util/misc.py
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@ -2,7 +2,6 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from scipy import weave
from config import *
def chain_1(df_dg, dg_dx):

64
GPy/util/univariate_Gaussian.py
View file

@ -12,6 +12,39 @@ def std_norm_cdf(x):
"""
Cumulative standard Gaussian distribution
Based on Abramowitz, M. and Stegun, I. (1970)
"""
x_shape = np.asarray(x).shape
if len(x_shape) == 0 or x_shape[0] == 1:
sign = np.sign(x)
x *= sign
x /= np.sqrt(2.)
t = 1.0/(1.0 + 0.3275911*x)
erf = 1. - np.exp(-x**2)*t*(0.254829592 + t*(-0.284496736 + t*(1.421413741 + t*(-1.453152027 + t*(1.061405429)))))
cdf_x = 0.5*(1.0 + sign*erf)
return cdf_x
else:
x = np.atleast_1d(x).copy()
cdf_x = np.zeros_like(x)
sign = np.ones_like(x)
neg_x_ind = x<0
sign[neg_x_ind] = -1.0
x[neg_x_ind] = -x[neg_x_ind]
x /= np.sqrt(2.)
t = 1.0/(1.0 + 0.3275911*x)
erf = 1. - np.exp(-x**2)*t*(0.254829592 + t*(-0.284496736 + t*(1.421413741 + t*(-1.453152027 + t*(1.061405429)))))
cdf_x = 0.5*(1.0 + sign*erf)
cdf_x = cdf_x.reshape(x_shape)
return cdf_x
def std_norm_cdf_weave(x):
"""
Cumulative standard Gaussian distribution
Based on Abramowitz, M. and Stegun, I. (1970)
A weave implementation of std_norm_cdf, which is faster. this is unused,
because of the difficulties of a weave dependency. (see github issue #94)
"""
#Generalize for many x
x = np.asarray(x).copy()
@ -40,37 +73,6 @@ def std_norm_cdf(x):
weave.inline(code, arg_names=['x', 'cdf_x', 'N'], support_code=support_code)
return cdf_x
def std_norm_cdf_np(x):
"""
Cumulative standard Gaussian distribution
Based on Abramowitz, M. and Stegun, I. (1970)
Around 3 times slower when x is a scalar otherwise quite a lot slower
"""
x_shape = np.asarray(x).shape
if len(x_shape) == 0 or x_shape[0] == 1:
sign = np.sign(x)
x *= sign
x /= np.sqrt(2.)
t = 1.0/(1.0 + 0.3275911*x)
erf = 1. - np.exp(-x**2)*t*(0.254829592 + t*(-0.284496736 + t*(1.421413741 + t*(-1.453152027 + t*(1.061405429)))))
cdf_x = 0.5*(1.0 + sign*erf)
return cdf_x
else:
x = np.atleast_1d(x).copy()
cdf_x = np.zeros_like(x)
sign = np.ones_like(x)
neg_x_ind = x<0
sign[neg_x_ind] = -1.0
x[neg_x_ind] = -x[neg_x_ind]
x /= np.sqrt(2.)
t = 1.0/(1.0 + 0.3275911*x)
erf = 1. - np.exp(-x**2)*t*(0.254829592 + t*(-0.284496736 + t*(1.421413741 + t*(-1.453152027 + t*(1.061405429)))))
cdf_x = 0.5*(1.0 + sign*erf)
cdf_x = cdf_x.reshape(x_shape)
return cdf_x
def inv_std_norm_cdf(x):
"""
Inverse cumulative standard Gaussian distribution