Source code for mars.tensor.random.f

#!/usr/bin/env python
# -*- coding: utf-8 -*-
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import numpy as np

from ... import opcodes as OperandDef
from ...serialization.serializables import AnyField
from ..utils import gen_random_seeds
from .core import TensorRandomOperandMixin, handle_array, TensorDistribution


class TensorF(TensorDistribution, TensorRandomOperandMixin):
    _input_fields_ = ['_dfnum', '_dfden']
    _op_type_ = OperandDef.RAND_F

    _fields_ = '_dfnum', '_dfden', '_size'
    _dfnum = AnyField('dfnum')
    _dfden = AnyField('dfden')
    _func_name = 'f'

    def __init__(self, size=None, dtype=None, **kw):
        dtype = np.dtype(dtype) if dtype is not None else dtype
        super().__init__(_size=size, dtype=dtype, **kw)

    @property
    def dfnum(self):
        return self._dfnum

    @property
    def dfden(self):
        return self._dfden

    def __call__(self, dfnum, dfden, chunk_size=None):
        return self.new_tensor([dfnum, dfden], None, raw_chunk_size=chunk_size)


[docs]def f(random_state, dfnum, dfden, size=None, chunk_size=None, gpu=None, dtype=None): """ Draw samples from an F distribution. Samples are drawn from an F distribution with specified parameters, `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom in denominator), where both parameters should be greater than zero. The random variate of the F distribution (also known as the Fisher distribution) is a continuous probability distribution that arises in ANOVA tests, and is the ratio of two chi-square variates. Parameters ---------- dfnum : float or array_like of floats Degrees of freedom in numerator, should be > 0. dfden : float or array_like of float Degrees of freedom in denominator, should be > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``dfnum`` and ``dfden`` are both scalars. Otherwise, ``np.broadcast(dfnum, dfden).size`` samples are drawn. chunk_size : int or tuple of int or tuple of ints, optional Desired chunk size on each dimension gpu : bool, optional Allocate the tensor on GPU if True, False as default dtype : data-type, optional Data-type of the returned tensor. Returns ------- out : Tensor or scalar Drawn samples from the parameterized Fisher distribution. See Also -------- scipy.stats.f : probability density function, distribution or cumulative density function, etc. Notes ----- The F statistic is used to compare in-group variances to between-group variances. Calculating the distribution depends on the sampling, and so it is a function of the respective degrees of freedom in the problem. The variable `dfnum` is the number of samples minus one, the between-groups degrees of freedom, while `dfden` is the within-groups degrees of freedom, the sum of the number of samples in each group minus the number of groups. References ---------- .. [1] Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill, Fifth Edition, 2002. .. [2] Wikipedia, "F-distribution", http://en.wikipedia.org/wiki/F-distribution Examples -------- An example from Glantz[1], pp 47-40: Two groups, children of diabetics (25 people) and children from people without diabetes (25 controls). Fasting blood glucose was measured, case group had a mean value of 86.1, controls had a mean value of 82.2. Standard deviations were 2.09 and 2.49 respectively. Are these data consistent with the null hypothesis that the parents diabetic status does not affect their children's blood glucose levels? Calculating the F statistic from the data gives a value of 36.01. Draw samples from the distribution: >>> import mars.tensor as mt >>> dfnum = 1. # between group degrees of freedom >>> dfden = 48. # within groups degrees of freedom >>> s = mt.random.f(dfnum, dfden, 1000).execute() The lower bound for the top 1% of the samples is : >>> sorted(s)[-10] 7.61988120985 So there is about a 1% chance that the F statistic will exceed 7.62, the measured value is 36, so the null hypothesis is rejected at the 1% level. """ if dtype is None: dtype = np.random.RandomState().f( handle_array(dfnum), handle_array(dfden), size=(0,)).dtype size = random_state._handle_size(size) seed = gen_random_seeds(1, random_state.to_numpy())[0] op = TensorF(seed=seed, size=size, gpu=gpu, dtype=dtype) return op(dfnum, dfden, chunk_size=chunk_size)