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
