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| -rw-r--r-- | doc/EXAMPLE_DOCSTRING.rst | 32 |
1 files changed, 16 insertions, 16 deletions
diff --git a/doc/EXAMPLE_DOCSTRING.rst b/doc/EXAMPLE_DOCSTRING.rst index 1de0588ec..d7ddf5260 100644 --- a/doc/EXAMPLE_DOCSTRING.rst +++ b/doc/EXAMPLE_DOCSTRING.rst @@ -21,22 +21,22 @@ Parameters ---------- mean : (N,) ndarray Mean of the N-dimensional distribution. -cov : (N,N) ndarray +cov : (N, N) ndarray Covariance matrix of the distribution. shape : tuple of ints, optional - Given a shape of, for example, (m,n,k), m*n*k samples are + Given a shape of, for example, (m, n, k), m*n*k samples are generated, and packed in an m-by-n-by-k arrangement. Because each - sample is N-dimensional, the output shape is (m,n,k,N). If no + sample is N-dimensional, the output shape is (m, n, k, N). If no shape is specified, a single sample is returned. Returns ------- out : ndarray The drawn samples, arranged according to `shape`. If the - shape given is (m,n,...), then the shape of `out` is - (m,n,...,N). + shape given is (m, n, ...), then the shape of `out` is + (m, n, ... , N). - In other words, each entry ``out[i,j,...,:]`` is an N-dimensional + In other words, each entry ``out[i, j, ... , :]`` is an N-dimensional value drawn from the distribution. See Also @@ -54,7 +54,7 @@ univariate normal distribution. Covariance indicates the level to which two variables vary together. From the multivariate normal distribution, we draw N-dimensional -samples, :math:`X = [x_1, x_2, ... x_N]`. The covariance matrix +samples, :math:`X = [x_1, x_2, ... , x_N]`. The covariance matrix element :math:`C_ij` is the covariance of :math:`x_i` and :math:`x_j`. The element :math:`C_ii` is the variance of :math:`x_i` (i.e. its "spread"). @@ -69,12 +69,12 @@ approximations include: This geometrical property can be seen in two dimensions by plotting generated data-points: ->>> mean = [0,0] ->>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis ->>> x,y = np.random.multivariate_normal(mean,cov,5000).T +>>> mean = [0, 0] +>>> cov = [[1, 0], [0, 100]] # diagonal covariance, points lie on x or y-axis +>>> x, y = np.random.multivariate_normal(mean, cov, 5000).T >>> import matplotlib.pyplot as plt ->>> plt.plot(x,y,'x'); plt.axis('equal'); pyplot.show() +>>> plt.plot(x, y, 'x'); plt.axis('equal'); plt.show() Note that the covariance matrix must be non-negative definite. @@ -87,17 +87,17 @@ References Examples -------- ->>> mean = (1,2) ->>> cov = [[1,0],[1,0]] ->>> x = np.random.multivariate_normal(mean,cov,(3,3)) +>>> mean = (1, 2) +>>> cov = [[1, 0], [0, 1]] +>>> x = np.random.multivariate_normal(mean, cov, (3, 3)) >>> x.shape (3, 3, 2) The following is probably true, given that 0.6 is roughly twice the standard deviation: ->>> print list( (x[0,0,:] - mean) < 0.6 ) -[True, True] +>>> print(list( (x[0, 0, :] - mean) < 0.6 )) +[True, True] # may vary .. index: :refguide: random:distributions |