| 8028 | | static char __pyx_doc_6mtrand_11RandomState_gamma[] = "\n"" gamma(shape, scale=1.0, size=None)\n""\n"" Draw samples from a Gamma distribution.\n""\n"" Samples are drawn from a Gamma distribution with specified parameters,\n"" `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n"" \"theta\"), where both parameters are > 0.\n""\n"" Parameters\n"" ----------\n"" shape : scalar > 0\n"" The shape of the gamma distribution.\n"" scale : scalar > 0, optional\n"" The scale of the gamma distribution. Default is equal to 1.\n"" size : shape_tuple, optional\n"" Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n"" ``m * n * k`` samples are drawn.\n""\n"" Returns\n"" -------\n"" out : ndarray, float\n"" Returns one sample unless `size` parameter is specified.\n""\n"" See Also\n"" --------\n"" scipy.stats.distributions.gamma : probability density function,\n"" distribution or cumulative density function, etc.\n""\n"" Notes\n"" -----\n"" The probability density for the Gamma distribution is\n""\n"" .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n""\n"" where :math:`k` is the shape and :math:`\\theta` the scale,\n"" and :math:`\\Gamma` is the Gamma function.\n""\n"" The Gamma distribution is often used to model the times to failure of\n"" electronic components, and arises naturally in processes for which the\n"" waiting times between Poisson distributed events are relevant.\n""\n"" References\n"" ----------\n"" .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n"" Wolfram Web Resource.\n"" http://mathworld.wolfram.com/GammaDistribution.html\n"" .. [2] Wikipedia, \"Gamma-distribution\",\n"" http://en.wikipedia.org/wiki/Gamma-distribution\n""\n"" Examples\n"" --------\n"" Draw samples from the distribution:\n""\n"" >>> shape, scale = 2., 2. # mean and dispersion\n"" >>> s = np.random.gamma(shape, scale, 1000)\n""\n"" Display the histogram of the samples, along with\n"" the probability density function:\n""\n"" >>> import matplotlib.pyplot as plt\n"" >>> import scipy.special as sps\n"" >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n"" >>> y = bins**(shape-1)*( |
| | 8028 | static char __pyx_doc_6mtrand_11RandomState_gamma[] = "\n"" gamma(shape, scale=1.0, size=None)\n""\n"" Draw samples from a Gamma distribution.\n""\n"" Samples are drawn from a Gamma distribution with specified parameters,\n"" `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n"" \"theta\"), where both parameters are > 0.\n""\n"" Parameters\n"" ----------\n"" shape : scalar > 0\n"" The shape of the gamma distribution.\n"" scale : scalar > 0, optional\n"" The scale of the gamma distribution. Default is equal to 1.\n"" size : shape_tuple, optional\n"" Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n"" ``m * n * k`` samples are drawn.\n""\n"" Returns\n"" -------\n"" out : ndarray, float\n"" Returns one sample unless `size` parameter is specified.\n""\n"" See Also\n"" --------\n"" scipy.stats.distributions.gamma : probability density function,\n"" distribution or cumulative density function, etc.\n""\n"" Notes\n"" -----\n"" The probability density for the Gamma distribution is\n""\n"" .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n""\n"" where :math:`k` is the shape and :math:`\\theta` the scale,\n"" and :math:`\\Gamma` is the Gamma function.\n""\n"" The Gamma distribution is often used to model the times to failure of\n"" electronic components, and arises naturally in processes for which the\n"" waiting times between Poisson distributed events are relevant.\n""\n"" References\n"" ----------\n"" .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n"" Wolfram Web Resource.\n"" http://mathworld.wolfram.com/GammaDistribution.html\n"" .. [2] Wikipedia, \"Gamma-distribution\",\n"" http://en.wikipedia.org/wiki/Gamma-distribution\n""\n"" Examples\n"" --------\n"" Draw samples from the distribution:\n""\n"" >>> shape, scale = 2., 2. # mean and dispersion\n"" >>> s = np.random.gamma(shape, scale, 1000)\n""\n"" Display the histogram of the samples, along with\n"" the probability density function:\n""\n"" >>> import matplotlib.pyplot as plt\n"" >>> import scipy.special as sps\n"" >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n"" >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n"" ... (sps.gamma(shape)*scale**shape))\n"" >>> plt.plot(bins, y, linewidth=2, color='r')\n"" >>> plt.show()\n""\n"" "; |
