tensorflow_probability/python/stats/sample_stats.py
def correlation(x,
                y=None,
                sample_axis=0,
                event_axis=-1,
                keepdims=False,
                name=None):
  """Sample correlation (Pearson) between observations indexed by `event_axis`.

  Given `N` samples of scalar random variables `X` and `Y`, correlation may be
  estimated as

  ```none
  Corr[X, Y] := Cov[X, Y] / Sqrt(Cov[X, X] * Cov[Y, Y]),
  where
  Cov[X, Y] := N^{-1} sum_{n=1}^N (X_n - Xbar) Conj{(Y_n - Ybar)}
  Xbar := N^{-1} sum_{n=1}^N X_n
  Ybar := N^{-1} sum_{n=1}^N Y_n
  ```

  Correlation is always in the interval `[-1, 1]`, and `Corr[X, X] == 1`.

  For vector-variate random variables `X = (X1, ..., Xd)`, `Y = (Y1, ..., Yd)`,
  one is often interested in the correlation matrix, `C_{ij} := Corr[Xi, Yj]`.

  ```python
  x = tf.random_normal(shape=(100, 2, 3))
  y = tf.random_normal(shape=(100, 2, 3))

  # corr[i, j] is the sample correlation between x[:, i, j] and y[:, i, j].
  corr = tfp.stats.correlation(x, y, sample_axis=0, event_axis=None)

  # corr_matrix[i, m, n] is the sample correlation of x[:, i, m] and y[:, i, n]
  corr_matrix = tfp.stats.correlation(x, y, sample_axis=0, event_axis=-1)
  ```

  Notice we divide by `N` (the numpy default), which does not create `NaN`
  when `N = 1`, but is slightly biased.

  Args:
    x:  A numeric `Tensor` holding samples.
    y:  Optional `Tensor` with same `dtype` and `shape` as `x`.
      Default value: `None` (`y` is effectively set to `x`).
    sample_axis: Scalar or vector `Tensor` designating axis holding samples, or
      `None` (meaning all axis hold samples).
      Default value: `0` (leftmost dimension).
    event_axis:  Scalar or vector `Tensor`, or `None` (scalar events).
      Axis indexing random events, whose correlation we are interested in.
      If a vector, entries must form a contiguous block of dims. `sample_axis`
      and `event_axis` should not intersect.
      Default value: `-1` (rightmost axis holds events).
    keepdims:  Boolean.  Whether to keep the sample axis as singletons.
    name: Python `str` name prefixed to Ops created by this function.
          Default value: `None` (i.e., `'correlation'`).

  Returns:
    corr: A `Tensor` of same `dtype` as the `x`, and rank equal to
      `rank(x) - len(sample_axis) + 2 * len(event_axis)`.

  Raises:
    AssertionError:  If `x` and `y` are found to have different shape.
    ValueError:  If `sample_axis` and `event_axis` are found to overlap.
    ValueError:  If `event_axis` is found to not be contiguous.
  """

  with tf.compat.v1.name_scope(
      name, 'correlation', values=[x, y, event_axis, sample_axis]):
    # Corr[X, Y] = Cov[X, Y] / (Stddev[X] * Stddev[Y])
    #            = Cov[X / Stddev[X], Y / Stddev[Y]]
    # So we could compute covariance first then divide by stddev, or
    # divide by stddev and compute covariance.
    # Dividing by stddev then computing covariance is potentially more stable.
    # But... computing covariance first then dividing involves 2 fewer large
    # broadcasts.  We choose to divide first, largely because it avoids
    # difficulties with the various options for sample/event axis kwargs.

    x /= stddev(x, sample_axis=sample_axis, keepdims=True)
    if y is not None:
      y /= stddev(y, sample_axis=sample_axis, keepdims=True)

    return covariance(
        x=x,
        y=y,
        event_axis=event_axis,
        sample_axis=sample_axis,
        keepdims=keepdims)
def correlation(x,
                y=None,
                sample_axis=0,
                event_axis=-1,
                keepdims=False,
                name=None):
  """Sample correlation (Pearson) between observations indexed by `event_axis`.

  Given `N` samples of scalar random variables `X` and `Y`, correlation may be
  estimated as

  ```none
  Corr[X, Y] := Cov[X, Y] / Sqrt(Cov[X, X] * Cov[Y, Y]),
  where
  Cov[X, Y] := N^{-1} sum_{n=1}^N (X_n - Xbar) Conj{(Y_n - Ybar)}
  Xbar := N^{-1} sum_{n=1}^N X_n
  Ybar := N^{-1} sum_{n=1}^N Y_n
  ```

  Correlation is always in the interval `[-1, 1]`, and `Corr[X, X] == 1`.

  For vector-variate random variables `X = (X1, ..., Xd)`, `Y = (Y1, ..., Yd)`,
  one is often interested in the correlation matrix, `C_{ij} := Corr[Xi, Yj]`.

  ```python
  x = tf.random_normal(shape=(100, 2, 3))
  y = tf.random_normal(shape=(100, 2, 3))

  # corr[i, j] is the sample correlation between x[:, i, j] and y[:, i, j].
  corr = tfp.stats.correlation(x, y, sample_axis=0, event_axis=None)

  # corr_matrix[i, m, n] is the sample correlation of x[:, i, m] and y[:, i, n]
  corr_matrix = tfp.stats.correlation(x, y, sample_axis=0, event_axis=-1)
  ```

  Notice we divide by `N` (the numpy default), which does not create `NaN`
  when `N = 1`, but is slightly biased.

  Args:
    x:  A numeric `Tensor` holding samples.
    y:  Optional `Tensor` with same `dtype` and `shape` as `x`.
      Default value: `None` (`y` is effectively set to `x`).
    sample_axis: Scalar or vector `Tensor` designating axis holding samples, or
      `None` (meaning all axis hold samples).
      Default value: `0` (leftmost dimension).
    event_axis:  Scalar or vector `Tensor`, or `None` (scalar events).
      Axis indexing random events, whose correlation we are interested in.
      If a vector, entries must form a contiguous block of dims. `sample_axis`
      and `event_axis` should not intersect.
      Default value: `-1` (rightmost axis holds events).
    keepdims:  Boolean.  Whether to keep the sample axis as singletons.
    name: Python `str` name prefixed to Ops created by this function.
          Default value: `None` (i.e., `'correlation'`).

  Returns:
    corr: A `Tensor` of same `dtype` as the `x`, and rank equal to
      `rank(x) - len(sample_axis) + 2 * len(event_axis)`.

  Raises:
    AssertionError:  If `x` and `y` are found to have different shape.
    ValueError:  If `sample_axis` and `event_axis` are found to overlap.
    ValueError:  If `event_axis` is found to not be contiguous.
  """

  with tf.compat.v1.name_scope(
      name, 'correlation', values=[x, y, event_axis, sample_axis]):
    # Corr[X, Y] = Cov[X, Y] / (Stddev[X] * Stddev[Y])
    #            = Cov[X / Stddev[X], Y / Stddev[Y]]
    # So we could compute covariance first then divide by stddev, or
    # divide by stddev and compute covariance.
    # Dividing by stddev then computing covariance is potentially more stable.
    # But... computing covariance first then dividing involves 2 fewer large
    # broadcasts.  We choose to divide first, largely because it avoids
    # difficulties with the various options for sample/event axis kwargs.

    x /= stddev(x, sample_axis=sample_axis, keepdims=True)
    if y is not None:
      y /= stddev(y, sample_axis=sample_axis, keepdims=True)

    return covariance(
        x=x,
        y=y,
        event_axis=event_axis,
        sample_axis=sample_axis,
        keepdims=keepdims)
def correlation ( x , y = None , sample_axis = 0 , event_axis = - 1 , keepdims = False , name = None ) : with tf . compat . v1 . name_scope ( name , 'correlation' , values = [ x , y , event_axis , sample_axis ] ) : # Corr[X, Y] = Cov[X, Y] / (Stddev[X] * Stddev[Y]) #            = Cov[X / Stddev[X], Y / Stddev[Y]] # So we could compute covariance first then divide by stddev, or # divide by stddev and compute covariance. # Dividing by stddev then computing covariance is potentially more stable. # But... computing covariance first then dividing involves 2 fewer large # broadcasts.  We choose to divide first, largely because it avoids # difficulties with the various options for sample/event axis kwargs. x /= stddev ( x , sample_axis = sample_axis , keepdims = True ) if y is not None : y /= stddev ( y , sample_axis = sample_axis , keepdims = True ) return covariance ( x = x , y = y , event_axis = event_axis , sample_axis = sample_axis , keepdims = keepdims )
Sample correlation (Pearson) between observations indexed by `event_axis`.

  Given `N` samples of scalar random variables `X` and `Y`, correlation may be
  estimated as

  ```none
  Corr[X, Y] := Cov[X, Y] / Sqrt(Cov[X, X] * Cov[Y, Y]),
  where
  Cov[X, Y] := N^{-1} sum_{n=1}^N (X_n - Xbar) Conj{(Y_n - Ybar)}
  Xbar := N^{-1} sum_{n=1}^N X_n
  Ybar := N^{-1} sum_{n=1}^N Y_n
  ```

  Correlation is always in the interval `[-1, 1]`, and `Corr[X, X] == 1`.

  For vector-variate random variables `X = (X1, ..., Xd)`, `Y = (Y1, ..., Yd)`,
  one is often interested in the correlation matrix, `C_{ij} := Corr[Xi, Yj]`.

  ```python
  x = tf.random_normal(shape=(100, 2, 3))
  y = tf.random_normal(shape=(100, 2, 3))

  # corr[i, j] is the sample correlation between x[:, i, j] and y[:, i, j].
  corr = tfp.stats.correlation(x, y, sample_axis=0, event_axis=None)

  # corr_matrix[i, m, n] is the sample correlation of x[:, i, m] and y[:, i, n]
  corr_matrix = tfp.stats.correlation(x, y, sample_axis=0, event_axis=-1)
  ```

  Notice we divide by `N` (the numpy default), which does not create `NaN`
  when `N = 1`, but is slightly biased.

  Args:
    x:  A numeric `Tensor` holding samples.
    y:  Optional `Tensor` with same `dtype` and `shape` as `x`.
      Default value: `None` (`y` is effectively set to `x`).
    sample_axis: Scalar or vector `Tensor` designating axis holding samples, or
      `None` (meaning all axis hold samples).
      Default value: `0` (leftmost dimension).
    event_axis:  Scalar or vector `Tensor`, or `None` (scalar events).
      Axis indexing random events, whose correlation we are interested in.
      If a vector, entries must form a contiguous block of dims. `sample_axis`
      and `event_axis` should not intersect.
      Default value: `-1` (rightmost axis holds events).
    keepdims:  Boolean.  Whether to keep the sample axis as singletons.
    name: Python `str` name prefixed to Ops created by this function.
          Default value: `None` (i.e., `'correlation'`).

  Returns:
    corr: A `Tensor` of same `dtype` as the `x`, and rank equal to
      `rank(x) - len(sample_axis) + 2 * len(event_axis)`.

  Raises:
    AssertionError:  If `x` and `y` are found to have different shape.
    ValueError:  If `sample_axis` and `event_axis` are found to overlap.
    ValueError:  If `event_axis` is found to not be contiguous.
Sample correlation ( Pearson ) between observations indexed by event_axis .