def normal_01_mean_test():
#*****************************************************************************80
#
## NORMAL_01_MEAN_TEST tests NORMAL_01_MEAN.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 03 March 2015
#
# Author:
#
# John Burkardt
#
import numpy as np
import platform
from normal_01_sample import normal_01_sample
from r8vec_max import r8vec_max
from r8vec_mean import r8vec_mean
from r8vec_min import r8vec_min
print('')
print('NORMAL_01_MEAN_TEST')
print(' Python version: %s' % (platform.python_version()))
print(' NORMAL_01_MEAN computes the Normal 01 mean;')
m = normal_01_mean()
print('')
print(' PDF mean = %14g' % (m))
nsample = 1000
seed = 123456789
x = np.zeros(nsample)
for i in range(0, nsample):
x[i], seed = normal_01_sample(seed)
ms = r8vec_mean(nsample, x)
xmax = r8vec_max(nsample, x)
xmin = r8vec_min(nsample, x)
print('')
print(' Sample size = %6d' % (nsample))
print(' Sample mean = %14g' % (ms))
print(' Sample maximum = %14g' % (xmax))
print(' Sample minimum = %14g' % (xmin))
#
# Terminate.
#
print('')
print('NORMAL_01_MEAN_TEST:')
print(' Normal end of execution.')
return
def normal_01_mean_test ( ):
#*****************************************************************************80
#
## NORMAL_01_MEAN_TEST tests NORMAL_01_MEAN.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 03 March 2015
#
# Author:
#
# John Burkardt
#
import numpy as np
from normal_01_sample import normal_01_sample
from r8vec_max import r8vec_max
from r8vec_mean import r8vec_mean
from r8vec_min import r8vec_min
print ''
print 'NORMAL_01_MEAN_TEST'
print ' NORMAL_01_MEAN computes the Normal 01 mean;'
m = normal_01_mean ( )
print ''
print ' PDF mean = %14g' % ( m )
nsample = 1000
seed = 123456789
x = np.zeros ( nsample )
for i in range ( 0, nsample ):
x[i], seed = normal_01_sample ( seed )
ms = r8vec_mean ( nsample, x )
xmax = r8vec_max ( nsample, x )
xmin = r8vec_min ( nsample, x )
print ''
print ' Sample size = %6d' % ( nsample )
print ' Sample mean = %14g' % ( ms )
print ' Sample maximum = %14g' % ( xmax )
print ' Sample minimum = %14g' % ( xmin )
print ''
print 'NORMAL_01_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
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 beta_sample_test():
#*****************************************************************************80
#
## BETA_SAMPLE_TEST tests BETA_MEAN, BETA_SAMPLE, BETA_VARIANCE.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 15 April 2009
#
# Author:
#
# John Burkardt
#
import numpy as np
import platform
from r8vec_max import r8vec_max
from r8vec_mean import r8vec_mean
from r8vec_min import r8vec_min
from r8vec_variance import r8vec_variance
nsample = 1000
seed = 123456789
print('')
print('BETA_SAMPLE_TEST')
print(' Python version: %s' % (platform.python_version()))
print(' BETA_MEAN computes the Beta mean')
print(' BETA_SAMPLE samples the Beta distribution')
print(' BETA_VARIANCE computes the Beta variance.')
a = 2.0
b = 3.0
check = beta_check(a, b)
if (not check):
print('')
print('BETA_SAMPLE_TEST - Fatal error!')
print(' The parameters are not legal.')
return
mean = beta_mean(a, b)
variance = beta_variance(a, b)
print('')
print(' PDF parameter A = %14g' % (a))
print(' PDF parameter B = %14g' % (b))
print(' PDF mean = %14g' % (mean))
print(' PDF variance = %14g' % (variance))
x = np.zeros(nsample)
for i in range(0, nsample):
x[i], seed = beta_sample(a, b, seed)
mean = r8vec_mean(nsample, x)
variance = r8vec_variance(nsample, x)
xmax = r8vec_max(nsample, x)
xmin = r8vec_min(nsample, x)
print('')
print(' Sample size = %6d' % (nsample))
print(' Sample mean = %14g' % (mean))
print(' Sample variance = %14g' % (variance))
print(' Sample maximum = %14g' % (xmax))
print(' Sample minimum = %14g' % (xmin))
#
# Terminate.
#
print('')
print('BETA_SAMPLE_TEST')
print(' Normal end of execution.')
return
def normal_ms_mean_test ( ):
#*****************************************************************************80
#
## NORMAL_MS_MEAN_TEST tests NORMAL_MS_MEAN.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 05 March 2015
#
# Author:
#
# John Burkardt
#
import numpy as np
from normal_ms_sample import normal_ms_sample
from r8vec_max import r8vec_max
from r8vec_mean import r8vec_mean
from r8vec_min import r8vec_min
print ''
print 'NORMAL_MS_MEAN_TEST'
print ' NORMAL_MS_MEAN computes the mean'
print ' of the Normal MS distribution.'
mu = 100.0
sigma = 15.0
m = normal_ms_mean ( mu, sigma )
print ''
print ' PDF parameter MU = %g' % ( mu )
print ' PDF parameter SIGMA = %g' % ( sigma )
print ' PDF mean = %g' % ( m )
nsample = 1000
seed = 123456789
x = np.zeros ( nsample )
for i in range ( 0, nsample ):
x[i], seed = normal_ms_sample ( mu, sigma, seed )
ms = r8vec_mean ( nsample, x )
xmax = r8vec_max ( nsample, x )
xmin = r8vec_min ( nsample, x )
print ''
print ' Sample size = %6d' % ( nsample )
print ' Sample mean = %14g' % ( ms )
print ' Sample maximum = %14g' % ( xmax )
print ' Sample minimum = %14g' % ( xmin )
print ''
print 'NORMAL_MS_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
def truncated_normal_ab_mean_test():
#*****************************************************************************80
#
## TRUNCATED_NORMAL_AB_MEAN_TEST tests TRUNCATED_NORMAL_AB_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_ab_sample import truncated_normal_ab_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
b = 150.0
mu = 100.0
sigma = 25.0
print ''
print 'TRUNCATED_NORMAL_AB_MEAN_TEST'
print ' TRUNCATED_NORMAL_AB_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,%g]' % (a, b)
m = truncated_normal_ab_mean(mu, sigma, a, b)
print ''
print ' PDF mean = %g' % (m)
x = np.zeros(sample_num)
for i in range(0, sample_num):
x[i], seed = truncated_normal_ab_sample(mu, sigma, a, 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_AB_MEAN_TEST:'
print ' Normal end of execution.'
return