o ?e @sdZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z dd lm Z dd l m Z dd l mZed e jd ejdfdd Zede jd ejdfddZdS)z Histograms. )dtypes)ops) array_ops)clip_ops)control_flow_assert)control_flow_ops) gen_math_ops)math_ops)dispatch) tf_exporthistogram_fixed_width_binsdNc Cst|d|||gutj|dd}t|}t|dg}tj|dd}tj|tjdd}t t |dd |g}t |g|}t ||j}t j||d|d |dd d}t j||d d} t t| d|d tj} t| |Wd S1swYd S)aBins the given values for use in a histogram. Given the tensor `values`, this operation returns a rank 1 `Tensor` representing the indices of a histogram into which each element of `values` would be binned. The bins are equal width and determined by the arguments `value_range` and `nbins`. Args: values: Numeric `Tensor`. value_range: Shape [2] `Tensor` of same `dtype` as `values`. values <= value_range[0] will be mapped to hist[0], values >= value_range[1] will be mapped to hist[-1]. nbins: Scalar `int32 Tensor`. Number of histogram bins. dtype: dtype for returned histogram. name: A name for this operation (defaults to 'histogram_fixed_width'). Returns: A `Tensor` holding the indices of the binned values whose shape matches `values`. Raises: TypeError: If any unsupported dtype is provided. tf.errors.InvalidArgumentError: If value_range does not satisfy value_range[0] < value_range[1]. Examples: >>> # Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) ... >>> nbins = 5 >>> value_range = [0.0, 5.0] >>> new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15] >>> indices = tf.histogram_fixed_width_bins(new_values, value_range, nbins=5) >>> indices.numpy() array([0, 0, 1, 2, 4, 4], dtype=int32) r values)name value_rangenbinsdtyperrznbins %s must > 0 scaled_valuesindicesN)r name_scopeZconvert_to_tensorrshapeZreshaperint32rAssertr ZgreaterrZwith_dependenciescastrtruedivfloorrZ clip_by_value) rrrrrrcheckZ nbins_floatrrr d/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/ops/histogram_ops.pyr s0+   $histogram_fixed_widthcCsLt|d|||g}tj|||||dWdS1swYdS)aReturn histogram of values. Given the tensor `values`, this operation returns a rank 1 histogram counting the number of entries in `values` that fell into every bin. The bins are equal width and determined by the arguments `value_range` and `nbins`. Args: values: Numeric `Tensor`. value_range: Shape [2] `Tensor` of same `dtype` as `values`. values <= value_range[0] will be mapped to hist[0], values >= value_range[1] will be mapped to hist[-1]. nbins: Scalar `int32 Tensor`. Number of histogram bins. dtype: dtype for returned histogram. name: A name for this operation (defaults to 'histogram_fixed_width'). Returns: A 1-D `Tensor` holding histogram of values. Raises: TypeError: If any unsupported dtype is provided. tf.errors.InvalidArgumentError: If value_range does not satisfy value_range[0] < value_range[1]. Examples: >>> # Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) ... >>> nbins = 5 >>> value_range = [0.0, 5.0] >>> new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15] >>> hist = tf.histogram_fixed_width(new_values, value_range, nbins=5) >>> hist.numpy() array([2, 1, 1, 0, 2], dtype=int32) r"rN)rrrZ_histogram_fixed_width)rrrrrr r r!r"gs) $)__doc__Ztensorflow.python.frameworkrrZtensorflow.python.opsrrrrrr Ztensorflow.python.utilr Z tensorflow.python.util.tf_exportr Zadd_dispatch_supportrr r"r r r r!s.          F