Move weights to be the second argument in tf.bincount.
This matches np.bincount(x, weights, minlength). Change: 153476565
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@ -73,13 +73,13 @@ class BincountTest(test_util.TensorFlowTestCase):
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else:
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weights = np.random.random(num_samples)
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self.assertAllEqual(
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math_ops.bincount(arr, weights=weights).eval(),
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math_ops.bincount(arr, weights).eval(),
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np.bincount(arr, weights))
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def test_zero_weights(self):
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with self.test_session():
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self.assertAllEqual(
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math_ops.bincount(np.arange(1000), weights=np.zeros(1000)).eval(),
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math_ops.bincount(np.arange(1000), np.zeros(1000)).eval(),
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np.zeros(1000))
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def test_negative(self):
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@ -2026,9 +2026,9 @@ def tanh(x, name=None):
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def bincount(arr,
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weights=None,
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minlength=None,
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maxlength=None,
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weights=None,
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dtype=dtypes.int32):
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"""Counts the number of occurrences of each value in an integer array.
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@ -2040,13 +2040,13 @@ def bincount(arr,
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Args:
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arr: An int32 tensor of non-negative values.
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weights: If non-None, must be the same shape as arr. For each value in
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`arr`, the bin will be incremented by the corresponding weight instead
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of 1.
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minlength: If given, ensures the output has length at least `minlength`,
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padding with zeros at the end if necessary.
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maxlength: If given, skips values in `arr` that are equal or greater than
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`maxlength`, ensuring that the output has length at most `maxlength`.
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weights: If non-None, must be the same shape as arr. For each value in
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`arr`, the bin will be incremented by the corresponding weight instead
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of 1.
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dtype: If `weights` is None, determines the type of the output bins.
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Returns:
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@ -650,7 +650,7 @@ tf_module {
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}
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member_method {
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name: "bincount"
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argspec: "args=[\'arr\', \'minlength\', \'maxlength\', \'weights\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \"<dtype: \'int32\'>\"], "
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argspec: "args=[\'arr\', \'weights\', \'minlength\', \'maxlength\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \"<dtype: \'int32\'>\"], "
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}
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member_method {
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name: "bitcast"
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