dpnp.corrcoef

dpnp.corrcoef(x, y=None, rowvar=True, *, dtype=None)[source]

Return Pearson product-moment correlation coefficients.

For full documentation refer to numpy.corrcoef.

Parameters:

x ({dpnp.ndarray, usm_ndarray}) -- A 1-D or 2-D array containing multiple variables and observations. Each row of x represents a variable, and each column a single observation of all those variables. Also see rowvar below.
y ({None, dpnp.ndarray, usm_ndarray}, optional) -- An additional set of variables and observations. y has the same shape as x. Default: None.
rowvar ({bool}, optional) -- If rowvar is True, then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations. Default: True.
dtype ({None, dtype}, optional) -- Data-type of the result. Default: None.

Returns:

R -- The correlation coefficient matrix of the variables.

Return type:

{dpnp.ndarray}

See also

dpnp.cov: Covariance matrix.

Examples

In this example we generate two random arrays, xarr and yarr, and compute the row-wise and column-wise Pearson correlation coefficients, R. Since rowvar is true by default, we first find the row-wise Pearson correlation coefficients between the variables of xarr.

>>> import dpnp as np
>>> np.random.seed(123)
>>> xarr = np.random.rand(3, 3).astype(np.float32)
>>> xarr
array([[7.2858386e-17, 2.2066992e-02, 3.9520904e-01],
       [4.8012391e-01, 5.9377134e-01, 4.5147297e-01],
       [9.0728188e-01, 9.9387854e-01, 5.8399546e-01]], dtype=float32)
>>> R1 = np.corrcoef(xarr)
>>> R1
array([[ 0.99999994, -0.6173796 , -0.9685411 ],
       [-0.6173796 ,  1.        ,  0.7937219 ],
       [-0.9685411 ,  0.7937219 ,  0.9999999 ]], dtype=float32)

If we add another set of variables and observations yarr, we can compute the row-wise Pearson correlation coefficients between the variables in xarr and yarr.

>>> yarr = np.random.rand(3, 3).astype(np.float32)
>>> yarr
array([[0.17615308, 0.65354985, 0.15716429],
       [0.09373496, 0.2123185 , 0.84086883],
       [0.9011005 , 0.45206687, 0.00225109]], dtype=float32)
>>> R2 = np.corrcoef(xarr, yarr)
>>> R2
array([[ 0.99999994, -0.6173796 , -0.968541  , -0.48613155,  0.9951523 ,
        -0.8900264 ],
       [-0.6173796 ,  1.        ,  0.7937219 ,  0.9875833 , -0.53702235,
         0.19083664],
       [-0.968541  ,  0.7937219 ,  0.9999999 ,  0.6883078 , -0.9393724 ,
         0.74857277],
       [-0.48613152,  0.9875833 ,  0.6883078 ,  0.9999999 , -0.39783284,
         0.0342579 ],
       [ 0.9951523 , -0.53702235, -0.9393725 , -0.39783284,  0.99999994,
        -0.9305482 ],
       [-0.89002645,  0.19083665,  0.7485727 ,  0.0342579 , -0.9305482 ,
         1.        ]], dtype=float32)

Finally if we use the option rowvar=False, the columns are now being treated as the variables and we will find the column-wise Pearson correlation coefficients between variables in xarr and yarr.

>>> R3 = np.corrcoef(xarr, yarr, rowvar=False)
>>> R3
array([[ 1.        ,  0.9724453 , -0.9909503 ,  0.8104691 , -0.46436927,
        -0.1643624 ],
       [ 0.9724453 ,  1.        , -0.9949381 ,  0.6515728 , -0.6580445 ,
         0.07012729],
       [-0.99095035, -0.994938  ,  1.        , -0.72450536,  0.5790461 ,
         0.03047091],
       [ 0.8104691 ,  0.65157276, -0.72450536,  1.        ,  0.14243561,
        -0.71102554],
       [-0.4643693 , -0.6580445 ,  0.57904613,  0.1424356 ,  0.99999994,
        -0.79727215],
       [-0.1643624 ,  0.07012729,  0.03047091, -0.7110255 , -0.7972722 ,
         0.99999994]], dtype=float32)