Python tensorflow.python.ops.math_ops.betainc() Examples
The following are 11
code examples of tensorflow.python.ops.math_ops.betainc().
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tensorflow.python.ops.math_ops
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Example #1
| Source File: binomial.py From lambda-packs with MIT License | 6 votes |
|
def _bdtr(k, n, p):
"""The binomial cumulative distribution function.
Args:
k: floating point `Tensor`.
n: floating point `Tensor`.
p: floating point `Tensor`.
Returns:
`sum_{j=0}^k p^j (1 - p)^(n - j)`.
"""
# Trick for getting safe backprop/gradients into n, k when
# betainc(a = 0, ..) = nan
# Write:
# where(unsafe, safe_output, betainc(where(unsafe, safe_input, input)))
ones = array_ops.ones_like(n - k)
k_eq_n = math_ops.equal(k, n)
safe_dn = array_ops.where(k_eq_n, ones, n - k)
dk = math_ops.betainc(a=safe_dn, b=k + 1, x=1 - p)
return array_ops.where(k_eq_n, ones, dk)
Example #2
| Source File: beta.py From lambda-packs with MIT License | 5 votes |
|
def _cdf(self, x):
return math_ops.betainc(self.concentration1, self.concentration0, x)
Example #3
| Source File: student_t.py From lambda-packs with MIT License | 5 votes |
|
def _cdf(self, x):
# Take Abs(scale) to make subsequent where work correctly.
y = (x - self.loc) / math_ops.abs(self.scale)
x_t = self.df / (y**2. + self.df)
neg_cdf = 0.5 * math_ops.betainc(0.5 * self.df, 0.5, x_t)
return array_ops.where(math_ops.less(y, 0.), neg_cdf, 1. - neg_cdf)
Example #4
| Source File: negative_binomial.py From lambda-packs with MIT License | 5 votes |
|
def _cdf(self, positive_counts):
if self.validate_args:
positive_counts = math_ops.floor(
distribution_util.embed_check_nonnegative_discrete(
positive_counts, check_integer=False))
return math_ops.betainc(
self.total_count, positive_counts + 1.,
math_ops.sigmoid(-self.logits))
Example #5
| Source File: beta.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def _cdf(self, x):
return math_ops.betainc(self.a, self.b, x)
Example #6
| Source File: student_t.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def _cdf(self, x):
# Take Abs(sigma) to make subsequent where work correctly.
y = (x - self.mu) / math_ops.abs(self.sigma)
x_t = self.df / (y**2. + self.df)
neg_cdf = 0.5 * math_ops.betainc(0.5 * self.df, 0.5, x_t)
return array_ops.where(math_ops.less(y, 0.), neg_cdf, 1. - neg_cdf)
Example #7
| Source File: beta.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def _cdf(self, x):
return math_ops.betainc(self.a, self.b, x)
Example #8
| Source File: beta.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def _cdf(self, x):
return math_ops.betainc(self.concentration1, self.concentration0, x)
Example #9
| Source File: student_t.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def _cdf(self, x):
# Take Abs(scale) to make subsequent where work correctly.
y = (x - self.loc) / math_ops.abs(self.scale)
x_t = self.df / (y**2. + self.df)
neg_cdf = 0.5 * math_ops.betainc(0.5 * self.df, 0.5, x_t)
return array_ops.where(math_ops.less(y, 0.), neg_cdf, 1. - neg_cdf)
Example #10
| Source File: beta.py From keras-lambda with MIT License | 5 votes |
|
def _cdf(self, x):
return math_ops.betainc(self.a, self.b, x)
Example #11
| Source File: student_t.py From keras-lambda with MIT License | 5 votes |
|
def _cdf(self, x):
# Take Abs(sigma) to make subsequent where work correctly.
y = (x - self.mu) / math_ops.abs(self.sigma)
x_t = self.df / (y**2. + self.df)
neg_cdf = 0.5 * math_ops.betainc(0.5 * self.df, 0.5, x_t)
return array_ops.where(math_ops.less(y, 0.), neg_cdf, 1. - neg_cdf)
