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+### `tf.contrib.metrics.streaming_pearson_correlation(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_pearson_correlation}
+
+Computes pearson correlation coefficient between `predictions`, `labels`.
+
+The `streaming_pearson_correlation` function delegates to
+`streaming_covariance` the tracking of three [co]variances:
+- streaming_covariance(predictions, labels), i.e. covariance
+- streaming_covariance(predictions, predictions), i.e. variance
+- streaming_covariance(labels, labels), i.e. variance
+
+The product-moment correlation ultimately returned is an idempotent operation
+`cov(predictions, labels) / sqrt(var(predictions) * var(labels))`. To
+facilitate correlation computation across multiple batches, the function
+groups the `update_op`s of the underlying streaming_covariance and returns an
+`update_op`.
+
+If `weights` is not None, then it is used to compute a weighted correlation.
+NOTE: these weights are treated as "frequency weights", as opposed to
+"reliability weights". See discussion of the difference on
+https://wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_variance
+
+##### Args:
+
+
+*  <b>`predictions`</b>: A `Tensor` of arbitrary size.
+*  <b>`labels`</b>: A `Tensor` of the same size as predictions.
+*  <b>`weights`</b>: An optional set of weights which indicates the frequency with which
+    an example is sampled. Must be broadcastable with `labels`.
+*  <b>`metrics_collections`</b>: An optional list of collections that the metric
+    value variable should be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that the metric update
+    ops should be added to.
+*  <b>`name`</b>: An optional variable_scope name.
+
+##### Returns:
+
+
+*  <b>`pearson_r`</b>: A tensor representing the current pearson product-moment
+    correlation coefficient, the value of
+    `cov(predictions, labels) / sqrt(var(predictions) * var(labels))`.
+*  <b>`update_op`</b>: An operation that updates the underlying variables appropriately.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If labels and predictions are of different sizes or if the
+    ignore_mask is of the wrong size or if either `metrics_collections` or
+    `updates_collections` are not a list or tuple.
+