def truncated_normal_b_cdf_inv_test():
#*****************************************************************************80
#
## TRUNCATED_NORMAL_B_CDF_INV_TEST tests TRUNCATED_NORMAL_B_CDF_INV.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 09 March 2015
#
# Author:
#
# John Burkardt
#
import platform
from truncated_normal_b_cdf import truncated_normal_b_cdf
from truncated_normal_b_sample import truncated_normal_b_sample
sample_num = 10
seed = 123456789
b = 150.0
mu = 100.0
sigma = 25.0
print('')
print('TRUNCATED_NORMAL_B_CDF_INV_TEST')
print(' Python version: %s' % (platform.python_version()))
print(' TRUNCATED_NORMAL_B_CDF_INV inverts the CDF of')
print(' 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 (-oo,%g]' % (b))
print('')
print(' X CDF CDF_INV')
print('')
for i in range(0, sample_num):
x, seed = truncated_normal_b_sample(mu, sigma, b, seed)
cdf = truncated_normal_b_cdf(x, mu, sigma, b)
x2 = truncated_normal_b_cdf_inv(cdf, mu, sigma, b)
print(' %14.6g %14.6g %14.6g' % (x, cdf, x2))
#
# Terminate.
#
print('')
print('TRUNCATED_NORMAL_B_CDF_INV_TEST')
print(' Normal end of execution.')
return
def truncated_normal_b_variance_test ( ):
#*****************************************************************************80
#
## TRUNCATED_NORMAL_B_VARIANCE_TEST tests TRUNCATED_NORMAL_B_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_b_sample import truncated_normal_b_sample
from r8vec_variance import r8vec_variance
sample_num = 1000
seed = 123456789
b = 150.0
mu = 100.0
sigma = 25.0
print ''
print 'TRUNCATED_NORMAL_B_VARIANCE_TEST'
print ' TRUNCATED_NORMAL_B_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 (-oo,%g]' % ( b )
value = truncated_normal_b_variance ( mu, sigma, b )
print ''
print ' PDF variance = %g' % ( value )
x = np.zeros ( sample_num )
for i in range ( 0, sample_num ):
x[i], seed = truncated_normal_b_sample ( mu, sigma, b, seed )
value = r8vec_variance ( sample_num, x )
print ''
print ' Sample size = %d' % ( sample_num )
print ' Sample variance = %g' % ( value )
print ''
print 'TRUNCATED_NORMAL_B_VARIANCE_TEST:'
print ' Normal end of execution.'
return
def truncated_normal_b_cdf_inv_test ( ):
#*****************************************************************************80
#
## TRUNCATED_NORMAL_B_CDF_INV_TEST tests TRUNCATED_NORMAL_B_CDF_INV.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 09 March 2015
#
# Author:
#
# John Burkardt
#
from truncated_normal_b_cdf import truncated_normal_b_cdf
from truncated_normal_b_sample import truncated_normal_b_sample
sample_num = 10
seed = 123456789
b = 150.0
mu = 100.0
sigma = 25.0
print ''
print 'TRUNCATED_NORMAL_B_CDF_INV_TEST'
print ' TRUNCATED_NORMAL_B_CDF_INV inverts the CDF of'
print ' 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 (-oo,%g]' % ( b )
print ''
print ' X CDF CDF_INV'
print ''
for i in range ( 0, sample_num ):
x, seed = truncated_normal_b_sample ( mu, sigma, b, seed )
cdf = truncated_normal_b_cdf ( x, mu, sigma, b )
x2 = truncated_normal_b_cdf_inv ( cdf, mu, sigma, b )
print ' %14.6g %14.6g %14.6g' % ( x, cdf, x2 )
print ''
print 'TRUNCATED_NORMAL_B_CDF_INV_TEST'
print ' Normal end of execution.'
return
def truncated_normal_b_mean_test ( ):
#*****************************************************************************80
#
## TRUNCATED_NORMAL_B_MEAN_TEST tests TRUNCATED_NORMAL_B_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_b_sample import truncated_normal_b_sample
from r8vec_max import r8vec_max
from r8vec_mean import r8vec_mean
from r8vec_min import r8vec_min
sample_num = 1000
seed = 123456789
b = 150.0
mu = 100.0
sigma = 25.0
print ''
print 'TRUNCATED_NORMAL_B_MEAN_TEST'
print ' TRUNCATED_NORMAL_B_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 (-oo,%g]' % ( b )
m = truncated_normal_b_mean ( mu, sigma, b )
print ''
print ' PDF mean = %g' % ( m )
x = np.zeros ( sample_num )
for i in range ( 0, sample_num ):
x[i], seed = truncated_normal_b_sample ( mu, sigma, b, 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_B_MEAN_TEST:'
print ' Normal end of execution.'
return
def truncated_normal_b_mean_test():
#*****************************************************************************80
#
## TRUNCATED_NORMAL_B_MEAN_TEST tests TRUNCATED_NORMAL_B_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_b_sample import truncated_normal_b_sample
from r8vec_max import r8vec_max
from r8vec_mean import r8vec_mean
from r8vec_min import r8vec_min
sample_num = 1000
seed = 123456789
b = 150.0
mu = 100.0
sigma = 25.0
print ''
print 'TRUNCATED_NORMAL_B_MEAN_TEST'
print ' TRUNCATED_NORMAL_B_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 (-oo,%g]' % (b)
m = truncated_normal_b_mean(mu, sigma, b)
print ''
print ' PDF mean = %g' % (m)
x = np.zeros(sample_num)
for i in range(0, sample_num):
x[i], seed = truncated_normal_b_sample(mu, sigma, b, 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_B_MEAN_TEST:'
print ' Normal end of execution.'
return
