def truncated_normal_a_variance_test ( ): #*****************************************************************************80 # ## TRUNCATED_NORMAL_A_VARIANCE_TEST tests TRUNCATED_NORMAL_A_VARIANCE. # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 08 March 2015 # # Author: # # John Burkardt # import numpy as np from truncated_normal_a_sample import truncated_normal_a_sample from r8vec_variance import r8vec_variance sample_num = 1000 seed = 123456789 a = 50.0 mu = 100.0 sigma = 25.0 print '' print 'TRUNCATED_NORMAL_A_VARIANCE_TEST' print ' TRUNCATED_NORMAL_A_VARIANCE computes the variance' print ' of the Truncated Normal distribution.' print '' print ' The "parent" normal distribution has' print ' mean = %g' % ( mu ) print ' standard deviation = %g' % ( sigma ) print ' The parent distribution is truncated to' print ' the interval [%g,+oo)' % ( a ) value = truncated_normal_a_variance ( mu, sigma, a ) print '' print ' PDF variance = %g' % ( value ) x = np.zeros ( sample_num ) for i in range ( 0, sample_num ): x[i], seed = truncated_normal_a_sample ( mu, sigma, a, seed ) value = r8vec_variance ( sample_num, x ) print '' print ' Sample size = %d' % ( sample_num ) print ' Sample variance = %g' % ( value ) print '' print 'TRUNCATED_NORMAL_A_VARIANCE_TEST:' print ' Normal end of execution.' return
def truncated_normal_a_cdf_inv_test ( ): #*****************************************************************************80 # ## TRUNCATED_NORMAL_A_CDF_INV_TEST tests TRUNCATED_NORMAL_A_CDF_INV. # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 09 March 2015 # # Author: # # John Burkardt # import platform from truncated_normal_a_cdf import truncated_normal_a_cdf from truncated_normal_a_sample import truncated_normal_a_sample sample_num = 10 seed = 123456789 a = 50.0 mu = 100.0 sigma = 25.0 print ( '' ) print ( 'TRUNCATED_NORMAL_A_CDF_INV_TEST' ) print ( ' Python version: %s' % ( platform.python_version ( ) ) ) print ( ' TRUNCATED_NORMAL_A_CDF_INV inverts the CDF of' ) print ( ' the lower Truncated Normal distribution.' ) print ( '' ) print ( ' The "parent" normal distribution has' ) print ( ' mean = %g' % ( mu ) ) print ( ' standard deviation = %g' % ( sigma ) ) print ( ' The parent distribution is truncated to' ) print ( ' the interval [%g,+oo)' % ( a ) ) print ( '' ) print ( ' X CDF CDF_INV' ) print ( '' ) for i in range ( 0, sample_num ): x, seed = truncated_normal_a_sample ( mu, sigma, a, seed ) cdf = truncated_normal_a_cdf ( x, mu, sigma, a ) x2 = truncated_normal_a_cdf_inv ( cdf, mu, sigma, a ) print ( ' %14.6g %14.6g %14.6g' % ( x, cdf, x2 ) ) # # Terminate. # print ( '' ) print ( 'TRUNCATED_NORMAL_A_CDF_INV_TEST' ) print ( ' Normal end of execution.' ) return
def truncated_normal_a_cdf_inv_test ( ): #*****************************************************************************80 # ## TRUNCATED_NORMAL_A_CDF_INV_TEST tests TRUNCATED_NORMAL_A_CDF_INV. # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 09 March 2015 # # Author: # # John Burkardt # from truncated_normal_a_cdf import truncated_normal_a_cdf from truncated_normal_a_sample import truncated_normal_a_sample sample_num = 10 seed = 123456789 a = 50.0 mu = 100.0 sigma = 25.0 print '' print 'TRUNCATED_NORMAL_A_CDF_INV_TEST' print ' TRUNCATED_NORMAL_A_CDF_INV inverts the CDF of' print ' the lower Truncated Normal distribution.' print '' print ' The "parent" normal distribution has' print ' mean = %g' % ( mu ) print ' standard deviation = %g' % ( sigma ) print ' The parent distribution is truncated to' print ' the interval [%g,+oo)' % ( a ) print '' print ' X CDF CDF_INV' print '' for i in range ( 0, sample_num ): x, seed = truncated_normal_a_sample ( mu, sigma, a, seed ) cdf = truncated_normal_a_cdf ( x, mu, sigma, a ) x2 = truncated_normal_a_cdf_inv ( cdf, mu, sigma, a ) print ' %14.6g %14.6g %14.6g' % ( x, cdf, x2 ) print '' print 'TRUNCATED_NORMAL_A_CDF_INV_TEST' print ' Normal end of execution.' return
def truncated_normal_a_mean_test(): #*****************************************************************************80 # ## TRUNCATED_NORMAL_A_MEAN_TEST tests TRUNCATED_NORMAL_A_MEAN. # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 08 March 2015 # # Author: # # John Burkardt # import numpy as np import platform from truncated_normal_a_sample import truncated_normal_a_sample from r8vec_max import r8vec_max from r8vec_mean import r8vec_mean from r8vec_min import r8vec_min sample_num = 1000 seed = 123456789 a = 50.0 mu = 100.0 sigma = 25.0 print('') print('TRUNCATED_NORMAL_A_MEAN_TEST') print(' Python version: %s' % (platform.python_version())) print(' TRUNCATED_NORMAL_A_MEAN computes the mean') print(' of the Truncated Normal distribution.') print('') print(' The "parent" normal distribution has') print(' mean = %g' % (mu)) print(' standard deviation = %g' % (sigma)) print(' The parent distribution is truncated to') print(' the interval [%g,+oo)' % (a)) m = truncated_normal_a_mean(mu, sigma, a) print('') print(' PDF mean = %g' % (m)) x = np.zeros(sample_num) for i in range(0, sample_num): x[i], seed = truncated_normal_a_sample(mu, sigma, a, seed) ms = r8vec_mean(sample_num, x) xmax = r8vec_max(sample_num, x) xmin = r8vec_min(sample_num, x) print('') print(' Sample size = %6d' % (sample_num)) print(' Sample mean = %14g' % (ms)) print(' Sample maximum = %14g' % (xmax)) print(' Sample minimum = %14g' % (xmin)) # # Terminate. # print('') print('TRUNCATED_NORMAL_A_MEAN_TEST:') print(' Normal end of execution.') return
def truncated_normal_a_mean_test ( ): #*****************************************************************************80 # ## TRUNCATED_NORMAL_A_MEAN_TEST tests TRUNCATED_NORMAL_A_MEAN. # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 08 March 2015 # # Author: # # John Burkardt # import numpy as np from truncated_normal_a_sample import truncated_normal_a_sample from r8vec_max import r8vec_max from r8vec_mean import r8vec_mean from r8vec_min import r8vec_min sample_num = 1000 seed = 123456789 a = 50.0 mu = 100.0 sigma = 25.0 print '' print 'TRUNCATED_NORMAL_A_MEAN_TEST' print ' TRUNCATED_NORMAL_A_MEAN computes the mean' print ' of the Truncated Normal distribution.' print '' print ' The "parent" normal distribution has' print ' mean = %g' % ( mu ) print ' standard deviation = %g' % ( sigma ) print ' The parent distribution is truncated to' print ' the interval [%g,+oo)' % ( a ) m = truncated_normal_a_mean ( mu, sigma, a ) print '' print ' PDF mean = %g' % ( m ) x = np.zeros ( sample_num ) for i in range ( 0, sample_num ): x[i], seed = truncated_normal_a_sample ( mu, sigma, a, seed ) ms = r8vec_mean ( sample_num, x ) xmax = r8vec_max ( sample_num, x ) xmin = r8vec_min ( sample_num, x ) print '' print ' Sample size = %6d' % ( sample_num ) print ' Sample mean = %14g' % ( ms ) print ' Sample maximum = %14g' % ( xmax ) print ' Sample minimum = %14g' % ( xmin ) print '' print 'TRUNCATED_NORMAL_A_MEAN_TEST:' print ' Normal end of execution.' return