Exemple #1
0
    def test_mvn_mvt_2(self):
        a, b = self.a, self.b
        df = self.df
        corr2 = self.corr2

        probmvn_R = 0.6472497 #reported error approx. 7.7e-08
        probmvt_R = 0.5881863 #highest reported error up to approx. 1.99e-06
        assert_almost_equal(probmvt_R, mvstdtprob(a, b, corr2, df), 4)
        assert_almost_equal(probmvn_R, mvstdnormcdf(a, b, corr2, abseps=1e-5), 4)
    def test_mvn_mvt_2(self):
        a, b = self.a, self.b
        df = self.df
        corr2 = self.corr2

        probmvn_R = 0.6472497 #reported error approx. 7.7e-08
        probmvt_R = 0.5881863 #highest reported error up to approx. 1.99e-06
        assert_almost_equal(probmvt_R, mvstdtprob(a, b, corr2, df), 4)
        assert_almost_equal(probmvn_R, mvstdnormcdf(a, b, corr2, abseps=1e-5), 4)
    def test_mvn_mvt_5(self):
        a, bl = self.a, self.b
        df = self.df
        corr2 = self.corr2

        #unequal integration bounds
        #print "ue"
        a3 = np.array([0.5, -0.5, 0.5])
        probmvn_R = 0.06910487 #using higher precision in R, error approx. 3.5e-08
        probmvt_R = 0.05797867 #using higher precision in R, error approx. 5.8e-08
        assert_almost_equal(mvstdtprob(a3, a3+1, corr2, df), probmvt_R, 4)
        assert_almost_equal(probmvn_R, mvstdnormcdf(a3, a3+1, corr2,
                                                maxpts=100000, abseps=1e-5), 4)
Exemple #4
0
    def test_mvn_mvt_5(self):
        a, bl = self.a, self.b
        df = self.df
        corr2 = self.corr2

        #unequal integration bounds
        #print "ue"
        a3 = np.array([0.5, -0.5, 0.5])
        probmvn_R = 0.06910487 #using higher precision in R, error approx. 3.5e-08
        probmvt_R = 0.05797867 #using higher precision in R, error approx. 5.8e-08
        assert_almost_equal(mvstdtprob(a3, a3+1, corr2, df), probmvt_R, 4)
        assert_almost_equal(probmvn_R, mvstdnormcdf(a3, a3+1, corr2,
                                                maxpts=100000, abseps=1e-5), 4)
    def test_mvn_mvt_3(self):
        a, b = self.a, self.b
        df = self.df
        corr2 = self.corr2

        #from -inf
        #print 'from -inf'
        a2 = a.copy()
        a2[:] = -np.inf
        probmvn_R = 0.9961141 #using higher precision in R, error approx. 6.866163e-07
        probmvt_R = 0.9522146 #using higher precision in R, error approx. 1.6e-07
        assert_almost_equal(probmvt_R, mvstdtprob(a2, b, corr2, df), 4)
        assert_almost_equal(probmvn_R, mvstdnormcdf(a2, b, corr2, maxpts=100000,
                                                    abseps=1e-5), 4)
Exemple #6
0
    def test_mvn_mvt_3(self):
        a, b = self.a, self.b
        df = self.df
        corr2 = self.corr2

        #from -inf
        #print 'from -inf'
        a2 = a.copy()
        a2[:] = -np.inf
        probmvn_R = 0.9961141 #using higher precision in R, error approx. 6.866163e-07
        probmvt_R = 0.9522146 #using higher precision in R, error approx. 1.6e-07
        assert_almost_equal(probmvt_R, mvstdtprob(a2, b, corr2, df), 4)
        assert_almost_equal(probmvn_R, mvstdnormcdf(a2, b, corr2, maxpts=100000,
                                                    abseps=1e-5), 4)
    def test_mvn_mvt_4(self):
        a, bl = self.a, self.b
        df = self.df
        corr2 = self.corr2

        #from 0 to inf
        #print '0 inf'
        a2 = a.copy()
        a2[:] = -np.inf
        probmvn_R = 0.1666667 #error approx. 6.1e-08
        probmvt_R = 0.1666667 #error approx. 8.2e-08
        assert_almost_equal(probmvt_R, mvstdtprob(np.zeros(3), -a2, corr2, df), 4)
        assert_almost_equal(probmvn_R,
                            mvstdnormcdf(np.zeros(3), -a2, corr2,
                                         maxpts=100000, abseps=1e-5), 4)
    def test_mvn_mvt_1(self):
        a, b = self.a, self.b
        df = self.df
        corr_equal = self.corr_equal
        #result from R, mvtnorm with option
        #algorithm = GenzBretz(maxpts = 100000, abseps = 0.000001, releps = 0)
        #     or higher
        probmvt_R = 0.60414   #report, ed error approx. 7.5e-06
        probmvn_R = 0.673970  #reported error approx. 6.4e-07
        assert_almost_equal(probmvt_R, mvstdtprob(a, b, corr_equal, df), 4)
        assert_almost_equal(probmvn_R,
                            mvstdnormcdf(a, b, corr_equal, abseps=1e-5), 4)

        mvn_high = mvstdnormcdf(a, b, corr_equal, abseps=1e-8, maxpts=10000000)
        assert_almost_equal(probmvn_R, mvn_high, 5)
Exemple #9
0
    def test_mvn_mvt_4(self):
        a, bl = self.a, self.b
        df = self.df
        corr2 = self.corr2

        #from 0 to inf
        #print '0 inf'
        a2 = a.copy()
        a2[:] = -np.inf
        probmvn_R = 0.1666667 #error approx. 6.1e-08
        probmvt_R = 0.1666667 #error approx. 8.2e-08
        assert_almost_equal(probmvt_R, mvstdtprob(np.zeros(3), -a2, corr2, df), 4)
        assert_almost_equal(probmvn_R,
                            mvstdnormcdf(np.zeros(3), -a2, corr2,
                                         maxpts=100000, abseps=1e-5), 4)
Exemple #10
0
    def test_mvn_mvt_1(self):
        a, b = self.a, self.b
        df = self.df
        corr_equal = self.corr_equal
        #result from R, mvtnorm with option
        #algorithm = GenzBretz(maxpts = 100000, abseps = 0.000001, releps = 0)
        #     or higher
        probmvt_R = 0.60414   #report, ed error approx. 7.5e-06
        probmvn_R = 0.673970  #reported error approx. 6.4e-07
        assert_almost_equal(probmvt_R, mvstdtprob(a, b, corr_equal, df), 4)
        assert_almost_equal(probmvn_R,
                            mvstdnormcdf(a, b, corr_equal, abseps=1e-5), 4)

        mvn_high = mvstdnormcdf(a, b, corr_equal, abseps=1e-8, maxpts=10000000)
        assert_almost_equal(probmvn_R, mvn_high, 5)
Exemple #11
0
    def cdf(self, x, **kwds):
        '''cumulative distribution function

        Parameters
        ----------
        x : array_like
            can be 1d or 2d, if 2d, then each row is taken as independent
            multivariate random vector
        kwds : dict
            contains options for the numerical calculation of the cdf

        Returns
        -------
        cdf : float or array
            probability density value of each random vector

        '''
        lower = -np.inf * np.ones_like(x)
        #std_sigma = np.sqrt(np.diag(self.sigma))
        upper = (x - self.mean) / self.std_sigma
        return mvstdtprob(lower, upper, self.corr, self.df, **kwds)
    def cdf(self, x, **kwds):
        '''cumulative distribution function

        Parameters
        ----------
        x : array_like
            can be 1d or 2d, if 2d, then each row is taken as independent
            multivariate random vector
        kwds : dict
            contains options for the numerical calculation of the cdf

        Returns
        -------
        cdf : float or array
            probability density value of each random vector

        '''
        lower = -np.inf * np.ones_like(x)
        #std_sigma = np.sqrt(np.diag(self.sigma))
        upper = (x - self.mean)/self.std_sigma
        return mvstdtprob(lower, upper, self.corr, self.df, **kwds)