Generate the natural logarithm of probability density function (PDF) of the univariate Normal distribution.
More...
Generate the natural logarithm of probability density function (PDF) of the univariate Normal distribution.
See the documentation of pm_distNorm for further details.
- Parameters
-
[in] | x | : The input scalar or array of the same shape as other array-like arguments, of the same type and kind as the output logPDF , representing the point(s) at which the PDF must be computed.
|
[in] | mu | : The input scalar or array of the same shape as other array-like arguments of the same type and kind as the output logPDF representing the mean of the distribution.
(optional, default = 0. ) |
[in] | sigma | : The input scalar of the same type and kind as the output logPDF representing the inverse of the standard deviation of the distribution.
(optional, default = 1. ) |
- Returns
logPDF
: The output scalar or array of the same shape as the input array-like arguments, of,
-
type
real
of kind any supported by the processor (e.g., RK, RK32, RK64, or RK128),
representing the natural logarithm of the PDF of the Normal distribution at the specified x
.
Possible calling interfaces ⛓
Generate the natural logarithm of probability density function (PDF) of the univariate Normal distrib...
This module contains classes and procedures for computing various statistical quantities related to t...
- Warning
- The condition
0. < sigma
must hold for the corresponding procedure argument.
This condition is verified only if the library is built with the preprocessor macro CHECK_ENABLED=1
.
-
The
pure
procedure(s) documented herein become impure
when the ParaMonte library is compiled with preprocessor macro CHECK_ENABLED=1
.
By default, these procedures are pure
in release
build and impure
in debug
and testing
builds.
- See also
- setNormLogPDF
getLogNormLogPDF
setLogNormLogPDF
Example usage ⛓
12 integer(IK),
parameter :: NP
= 1000_IK
13 real(RK), dimension(NP) :: point, mu, Sigma, logPDF
15 type(display_type) :: disp
20 call setLogSpace(Sigma, logx1
= log(
0.1_RK), logx2
= log(
10._RK))
23 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
24 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
25 call disp%show(
"! Compute the Probability Density Function (PDF) of the (Standard) Normal distribution.")
26 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
27 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
39 call disp%show(
"logPDF(1) = getNormLogPDF(point(NP/2))")
56 call disp%show(
"logPDF(1) = getNormLogPDF(point(1), mu(1))")
63 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
64 call disp%show(
"! PDF with a standard deviation.")
65 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
73 call disp%show(
"logPDF(1) = getNormLogPDF(point(1), Sigma(1))")
80 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
81 call disp%show(
"! PDF with a mean and a standard deviation.")
82 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
92 call disp%show(
"logPDF(1) = getNormLogPDF(point(1), mu(1), Sigma(1))")
99 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
100 call disp%show(
"! A vector of PDF at different points with the same mean and standard deviation.")
101 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
111 call disp%show(
"logPDF(1:NP:NP/5) = getNormLogPDF(point(1:NP:NP/5), mu(1), Sigma(1))")
112 logPDF(
1:NP:NP
/5)
= getNormLogPDF(point(
1:NP:NP
/5), mu(
1), Sigma(
1))
118 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
119 call disp%show(
"! A vector of PDF at the same point but with different means and standard deviations.")
120 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
130 call disp%show(
"logPDF(1:NP:NP/5) = getNormLogPDF(point(1), mu(1:NP:NP/5), Sigma(1:NP:NP/5))")
131 logPDF(
1:NP:NP
/5)
= getNormLogPDF(point(
1), mu(
1:NP:NP
/5), Sigma(
1:NP:NP
/5))
137 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
138 call disp%show(
"! A vector of PDF at different points with different means and a standard deviations.")
139 call disp%show(
"!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
149 call disp%show(
"logPDF(1:NP:NP/5) = getNormLogPDF(point(1:NP:NP/5), mu(1:NP:NP/5), Sigma(1:NP:NP/5))")
150 logPDF(
1:NP:NP
/5)
= getNormLogPDF(point(
1:NP:NP
/5), mu(
1:NP:NP
/5), Sigma(
1:NP:NP
/5))
160 integer(IK) :: fileUnit, i
161 open(newunit
= fileUnit, file
= "getNormLogPDF.RK.txt")
163 write(fileUnit,
"(*(f0.8,:,','))") point(i)
&
Return the linSpace output argument with size(linSpace) elements of evenly-spaced values over the int...
Return the logSpace output argument with size(logSpace) elements of logarithmically-evenly-spaced val...
This is a generic method of the derived type display_type with pass attribute.
This is a generic method of the derived type display_type with pass attribute.
This module contains procedures and generic interfaces for generating arrays with linear or logarithm...
This module contains classes and procedures for input/output (IO) or generic display operations on st...
type(display_type) disp
This is a scalar module variable an object of type display_type for general display.
This module defines the relevant Fortran kind type-parameters frequently used in the ParaMonte librar...
integer, parameter RK
The default real kind in the ParaMonte library: real64 in Fortran, c_double in C-Fortran Interoperati...
integer, parameter IK
The default integer kind in the ParaMonte library: int32 in Fortran, c_int32_t in C-Fortran Interoper...
integer, parameter SK
The default character kind in the ParaMonte library: kind("a") in Fortran, c_char in C-Fortran Intero...
Generate and return an object of type display_type.
Example Unix compile command via Intel ifort
compiler ⛓
3ifort -fpp -standard-semantics -O3 -Wl,-rpath,../../../lib -I../../../inc main.F90 ../../../lib/libparamonte* -o main.exe
Example Windows Batch compile command via Intel ifort
compiler ⛓
2set PATH=..\..\..\lib;%PATH%
3ifort /fpp /standard-semantics /O3 /I:..\..\..\include main.F90 ..\..\..\lib\libparamonte*.lib /exe:main.exe
Example Unix / MinGW compile command via GNU gfortran
compiler ⛓
3gfortran -cpp -ffree-line-length-none -O3 -Wl,-rpath,../../../lib -I../../../inc main.F90 ../../../lib/libparamonte* -o main.exe
Example output ⛓
15-0.10010010010010006E-1
75-10.000000000000000,
-5.9959959959959956,
-1.9919919919919913,
+2.0120120120120113,
+6.0160160160160174
76logPDF(
1:NP:NP
/5)
= getNormLogPDF(point(
1:NP:NP
/5), mu(
1), Sigma(
1))
78-1248.6163534402099,
-48.216754642213360,
-451.02196225222571,
-2457.0319762702461,
-6066.2467966962759
87-5.0000000000000000,
-2.9979979979979978,
-0.99599599599599564,
+1.0060060060060056,
+3.0080080080080087
89+0.10000000000000003,
+0.25142033481427983,
+0.63212184758124557,
+1.5892828656229783,
+3.9957803018952722
92logPDF(
1:NP:NP
/5)
= getNormLogPDF(point(
1), mu(
1:NP:NP
/5), Sigma(
1:NP:NP
/5))
94-1248.6163534402099,
-387.34354210840831,
-101.90739462144076,
-25.361001956632634,
-7.6031099743289019
103-5.0000000000000000,
-2.9979979979979978,
-0.99599599599599564,
+1.0060060060060056,
+3.0080080080080087
105+0.10000000000000003,
+0.25142033481427983,
+0.63212184758124557,
+1.5892828656229783,
+3.9957803018952722
107-10.000000000000000,
-5.9959959959959956,
-1.9919919919919913,
+2.0120120120120113,
+6.0160160160160174
108logPDF(
1:NP:NP
/5)
= getNormLogPDF(point(
1:NP:NP
/5), mu(
1:NP:NP
/5), Sigma(
1:NP:NP
/5))
110-1248.6163534402099,
-70.632134384964985,
-1.7015849550577211,
-1.5825619177830466,
-2.5875284310266533
Postprocessing of the example output ⛓
3import matplotlib.pyplot
as plt
16xlab = {
"CK" :
"X ( real/imaginary components )"
17 ,
"IK" :
"X ( integer-valued )"
18 ,
"RK" :
"X ( real-valued )"
20legends = [
r"$\mu = 0.0,~\sigma = 3.0$"
21 ,
r"$\mu = 0.0,~\sigma = 1.0$"
22 ,
r"$\mu = 0.0,~\sigma = 0.3$"
23 ,
r"$\mu = -2.,~\sigma = 1.0$"
26for kind
in [
"IK",
"CK",
"RK"]:
28 pattern =
"*." + kind +
".txt"
29 fileList = glob.glob(pattern)
30 if len(fileList) == 1:
32 df = pd.read_csv(fileList[0], delimiter =
",")
34 fig = plt.figure(figsize = 1.25 * np.array([6.4, 4.8]), dpi = 200)
38 plt.plot( df.values[:, 0]
41 , linewidth = linewidth
44 plt.plot( df.values[:, 1]
47 , linewidth = linewidth
51 plt.plot( df.values[:, 0]
54 , linewidth = linewidth
61 plt.xticks(fontsize = fontsize - 2)
62 plt.yticks(fontsize = fontsize - 2)
63 ax.set_xlabel(xlab[kind], fontsize = 17)
64 ax.set_ylabel(
"Probability Density Function (PDF)", fontsize = 17)
66 plt.grid(visible =
True, which =
"both", axis =
"both", color =
"0.85", linestyle =
"-")
67 ax.tick_params(axis =
"y", which =
"minor")
68 ax.tick_params(axis =
"x", which =
"minor")
70 plt.savefig(fileList[0].replace(
".txt",
".png"))
72 elif len(fileList) > 1:
74 sys.exit(
"Ambiguous file list exists.")
Visualization of the example output ⛓
- Test:
- test_pm_distNorm
Final Remarks ⛓
If you believe this algorithm or its documentation can be improved, we appreciate your contribution and help to edit this page's documentation and source file on GitHub.
For details on the naming abbreviations, see this page.
For details on the naming conventions, see this page.
This software is distributed under the MIT license with additional terms outlined below.
-
If you use any parts or concepts from this library to any extent, please acknowledge the usage by citing the relevant publications of the ParaMonte library.
-
If you regenerate any parts/ideas from this library in a programming environment other than those currently supported by this ParaMonte library (i.e., other than C, C++, Fortran, MATLAB, Python, R), please also ask the end users to cite this original ParaMonte library.
This software is available to the public under a highly permissive license.
Help us justify its continued development and maintenance by acknowledging its benefit to society, distributing it, and contributing to it.
- Copyright
- Computational Data Science Lab
- Author:
- Amir Shahmoradi, Oct 16, 2009, 11:14 AM, Michigan
Definition at line 294 of file pm_distNorm.F90.