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tfp.stats.histogram

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Count how often x falls in intervals defined by edges.

tfp.stats.histogram(
    x,
    edges,
    axis=None,
    extend_lower_interval=False,
    extend_upper_interval=False,
    dtype=None,
    name=None
)

Given edges = [c0, ..., cK], defining intervals I0 = [c0, c1), I1 = [c1, c2), ..., I_{K-1} = [c_{K-1}, cK], This function counts how often x falls into each interval.

Values of x outside of the intervals cause errors. Consider using extend_lower_interval, extend_upper_interval to deal with this.

Args:

  • x: Numeric N-D Tensor with N > 0. If axis is not None, must have statically known number of dimensions. The axis kwarg determines which dimensions index iid samples. Other dimensions of x index "events" for which we will compute different histograms.
  • edges: Tensor of same dtype as x. The first dimension indexes edges of intervals. Must either be 1-D or have edges.shape[1:] the same as the dimensions of x excluding axis. If rank(edges) > 1, edges[k] designates a shape edges.shape[1:] Tensor of interval edges for the corresponding dimensions of x.
  • axis: Optional 0-D or 1-D integer Tensor with constant values. The axis in x that index iid samples. Default value: None (treat every dimension as sample dimension).
  • extend_lower_interval: Python bool. If True, extend the lowest interval I0 to (-inf, c1].
  • extend_upper_interval: Python bool. If True, extend the upper interval I_{K-1} to [c_{K-1}, +inf).
  • dtype: The output type (int32 or int64). Default value: x.dtype.
  • name: A Python string name to prepend to created ops. Default value: 'histogram'

Returns:

  • counts: Tensor of type dtype and, with ~axis = [i for i in range(arr.ndim) if i not in axis], counts.shape = [edges.shape[0]] + x.shape[~axis]. With I a multi-index into ~axis, counts[k][I] is the number of times event(s) fell into the kth interval of edges.

Examples

# x.shape = [1000, 2]
# x[:, 0] ~ Uniform(0, 1), x[:, 1] ~ Uniform(1, 2).
x = tf.stack([tf.random_uniform([1000]), 1 + tf.random_uniform([1000])],
             axis=-1)

# edges ==> bins [0, 0.5), [0.5, 1.0), [1.0, 1.5), [1.5, 2.0].
edges = [0., 0.5, 1.0, 1.5, 2.0]

tfp.stats.histogram(x, edges)
==> approximately [500, 500, 500, 500]

tfp.stats.histogram(x, edges, axis=0)
==> approximately [[500, 500, 0, 0], [0, 0, 500, 500]]