Lines Matching defs:rate
54 The Poisson distribution is parameterized by an event `rate` parameter.
65 where `rate = lambda` and `Z` is the normalizing constant.
78 rate=None,
86 rate: Floating point tensor, the rate parameter. `rate` must be positive.
87 Must specify exactly one of `rate` and `log_rate`.
88 log_rate: Floating point tensor, the log of the rate parameter.
89 Must specify exactly one of `rate` and `log_rate`.
101 ValueError: if none or both of `rate`, `log_rate` are specified.
102 TypeError: if `rate` is not a float-type.
106 with ops.name_scope(name, values=[rate]) as name:
107 if (rate is None) == (log_rate is None):
108 raise ValueError("Must specify exactly one of `rate` and `log_rate`.")
110 rate = ops.convert_to_tensor(rate, name="rate")
111 if not rate.dtype.is_floating:
112 raise TypeError("rate.dtype ({}) is a not a float-type.".format(
113 rate.dtype.name))
114 with ops.control_dependencies([check_ops.assert_positive(rate)] if
116 self._rate = array_ops.identity(rate, name="rate")
117 self._log_rate = math_ops.log(rate, name="log_rate")
123 self._rate = math_ops.exp(log_rate, name="rate")
135 def rate(self):
136 """Rate parameter."""
141 """Log rate parameter."""
145 return array_ops.shape(self.rate)
148 return self.rate.shape
168 return math_ops.igammac(1. + x, self.rate)
171 return self.rate
179 return array_ops.identity(self.rate)
182 return array_ops.identity(self.rate)
185 """Note: when `rate` is an integer, there are actually two modes: `rate`
186 and `rate - 1`. In this case we return the larger, i.e., `rate`.""")
188 return math_ops.floor(self.rate)
192 self.rate, [n], dtype=self.dtype, seed=seed)
