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Diffstat (limited to 'External_Functions')
| -rw-r--r-- | External_Functions/gsl.mo | 939 |
1 files changed, 935 insertions, 4 deletions
diff --git a/External_Functions/gsl.mo b/External_Functions/gsl.mo index fb3683c..169fd75 100644 --- a/External_Functions/gsl.mo +++ b/External_Functions/gsl.mo @@ -3096,14 +3096,452 @@ end gsl_sf_bessel_jl_e; package STATISTICS package chap_21_1 - function gsl_stats_mean + function gsl_stats_mean"This function returns the arithmetic mean of data, a dataset of length n with stride stride." input Real data[:]; input Integer stride; input Integer n; output Real mean; - external "C" mean=gsl_stats_mean( data[:], stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Include="#include<gsl/gsl_statistics_int.h>", Library="gsl",Library="gslcblas"); + external "C" mean=gsl_stats_mean( data, stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>", Library="gsl",Library="gslcblas"); end gsl_stats_mean; + + function gsl_stats_variance"This function returns the estimated, or sample, variance of data, a dataset of length n with stride stride." + input Real data[:]; + input Integer stride; + input Integer n; + output Real variance; + external "C" variance=gsl_stats_variance(data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_variance; + + function gsl_stats_variance_m"This function returns the sample variance of data relative to the value of mean calculated using the function gsl_stats_mean." + input Real data[:]; + input Integer stride; + input Integer n; + input Real mean; + output Real variance_m; + external "C" variance_m=gsl_stats_variance_m(data ,stride,n,mean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_variance_m; + + function gsl_stats_sd"The function returns the standard deviation of the given data set of length n and stride 'stride'" + input Real data[:]; + input Integer stride; + input Integer n; + output Real sd; + external "C" sd=gsl_stats_sd(data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_sd; + + function gsl_stats_sd_m"This function returns the sample standard deviation of data relative to the value of mean calculated using the function gsl_stats_mean." + input Real data[:]; + input Integer stride; + input Integer n; + input Real mean; + output Real sd_m; + external "C" sd_m=gsl_stats_sd_m(data ,stride,n,mean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_sd_m; + + function gsl_stats_tss"This function returns the total sum of squares(TSS) of a dataset of length n with stride stride." + input Real data[:]; + input Integer stride; + input Integer n; + output Real tss"total sum of squares"; + external "C" tss=gsl_stats_tss(data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_tss; + + function gsl_stats_tss_m"This function returns the sample total sum of squares of data relative to the value of mean(calculated from the function gsl_stats_mean) for a given data set with length n and given stride" + input Real data[:]; + input Integer stride; + input Integer n; + input Real mean; + output Real tss_m; + external "C" tss_m=gsl_stats_tss_m(data ,stride,n,mean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_tss_m; + + function gsl_stats_variance_with_fixed_mean"This function calculates the variance of a dataset with the population mean known a priori" + input Real data[:]; + input Integer stride; + input Integer n; + input Real mean; + output Real variance; + external "C" variance=gsl_stats_variance_with_fixed_mean(data,stride,n,mean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_variance_with_fixed_mean; + + function gsl_stats_sd_with_fixed_mean"This function calculates the standard deviation of a dataset with the population mean known a priori" + input Real data[:]; + input Integer stride; + input Integer n; + input Real mean; + output Real sd; + external "C" sd=gsl_stats_sd_with_fixed_mean(data,stride,n,mean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_sd_with_fixed_mean; + end chap_21_1; + + package chap_21_2 + function gsl_stats_absdev"This function computes the absolute deviation from mean of the data and given length n and stride " + input Real data[:]; + input Integer stride; + input Integer n; + output Real absdev; + external "C" absdev=gsl_stats_absdev(data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_absdev; + + function gsl_stats_absdev_m"This function computes the absolute deviation of data set with the mean ,stride,length given" + input Real data[:]; + input Integer stride; + input Integer n; + input Real mean; + output Real absdev_m; + external "C" absdev_m=gsl_stats_absdev_m(data,stride,n,mean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_absdev_m; + end chap_21_2; + package chap_21_3 + function gsl_stats_skew"This function computes the skewness of data, a dataset of length n with stride stride." + input Real data[:]; + input Integer stride; + input Integer n; + output Real skew; + external "C" skew=gsl_stats_skew(data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_skew; + + function gsl_stats_skew_m_sd"This function computes the skewness of the dataset data using the given values of the mean mean and standard + deviation sd" + input Real data[:]; + input Integer stride; + input Integer n; + input Real mean; + input Real sd; + output Real skew; + external"C" skew= gsl_stats_skew_m_sd(data,stride,n,mean,sd)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_skew_m_sd; + + function gsl_stats_kurtosis"This function computes the kurtosis of data, a dataset of length n with stride stride" + input Real data[:]; + input Integer stride; + input Integer n; + output Real kurtosis; + external "C" kurtosis=gsl_stats_kurtosis(data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_kurtosis; + + + function gsl_stats_kurtosis_m_sd"This function computes the kurtosis of the dataset data using the given values of the mean mean and standard + deviation sd" + input Real data[:]; + input Integer stride; + input Integer n; + input Real mean; + input Real sd; + output Real kurtosis; + external"C" kurtosis= gsl_stats_kurtosis_m_sd(data,stride,n,mean,sd)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_kurtosis_m_sd; + end chap_21_3; + package chap_21_4 + function gsl_stats_lag1_autocorrelation"This function computes the lag-1 autocorrelation of the dataset data given stride and length n" + input Real data[:]; + input Integer stride; + input Integer n; + output Real lag1; + external "C" lag1=gsl_stats_lag1_autocorrelation(data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_lag1_autocorrelation; + + function gsl_stats_lag1_autocorrelation_m"This function computes the lag-1 autocorrelation of the dataset data using the given value of the mean mean,stride and length" + input Real data[:]; + input Integer stride; + input Integer n; + input Real mean; + output Real lag1; + external "C" lag1=gsl_stats_lag1_autocorrelation_m(data,stride,n,mean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_lag1_autocorrelation_m; + end chap_21_4; + + package chap_21_5 + function gsl_stats_covariance"This function computes the covariance of data sets data1 and data2 given their strides and length n" + input Real data1[:]; + input Integer stride1; + input Real data2[:]; + input Integer stride2; + input Integer n; + output Real covar; + external "C" covar=gsl_stats_covariance(data1,stride1,data2,stride2,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_covariance; + + function gsl_stats_covariance_m"This function computes the covariance of data sets data1 and data2 given their strides and length n and mean1 and mean2" + input Real data1[:]; + input Integer stride1; + input Real data2[:]; + input Integer stride2; + input Integer n; + input Real mean1; + input Real mean2; + output Real covar; + external "C" covar=gsl_stats_covariance_m(data1,stride1,data2,stride2,n,mean1,mean2)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_covariance_m; + end chap_21_5; + package chap_21_6"correlation" + function gsl_stats_correlation"This function efficiently computes the Pearson correlation coefficient between the datasets data1 and data2 + which must both be of the same length n." + input Real data1[:]; + input Integer stride1; + input Real data2[:]; + input Integer stride2; + input Integer n; + output Real r"pearson correlation coefficient"; + external "C" r=gsl_stats_correlation(data1,stride1,data2,stride2,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_correlation; + + function gsl_stats_spearman"This function computes the Spearman rank correlation coefficient between the datasets data1 and data2 + which must both be of the same length n. Additional workspace of size 2 * n is required in work. The + Spearman rank correlation between vectors x and y is equivalent to the Pearson correlation between the ranked + vectors x R and y R , where ranks are defined to be the average of the positions of an element in the ascending + order of the values." + input Real data1[:]; + input Integer stride1; + input Real data2[:]; + input Integer stride2; + input Integer n; + output Real work[2*n]; + output Real y; + external "C" y=gsl_stats_spearman(data1,stride1,data2,stride2,n,work)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_spearman; + end chap_21_6; + package chap_21_7"Weighted Samples" + function gsl_stats_wmean"This functiton finds the weighted mean given the weight array, stride of the weight ,data ,its stride and length of the data " + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + output Real wmean; + external "C" wmean=gsl_stats_wmean(w,wstride,data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wmean; + + function gsl_stats_wvariance"This functiton finds the weighted variance given the weight array, stride of the weight ,data ,its stride and length of the data" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + output Real wvariance; + external "C" wvariance=gsl_stats_wvariance(w,wstride,data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wvariance; + + function gsl_stats_wvariance_m"This functiton finds the weighted variance given the weight array, stride of the weight ,data ,its stride , length of the data and the weighted mean" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + input Real wmean; + output Real wvariance; + external "C" wvariance=gsl_stats_wvariance_m(w,wstride,data,stride,n,wmean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + + end gsl_stats_wvariance_m; + + + function gsl_stats_wsd"This function calculates the weighted standard deviation given the weight array,stride of the weights,the data set its stride and length of the dataset" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + output Real wsd; + external "C" wsd=gsl_stats_wsd(w,wstride,data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wsd; + + + function gsl_stats_wsd_m"This functiton finds the weighted standard deviation given the weight array, stride of the weight ,data ,its stride , length of the data and the weighted mean" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + input Real wmean; + output Real wsd; + external "C" wsd=gsl_stats_wsd_m(w,wstride,data,stride,n,wmean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + + end gsl_stats_wsd_m; + + function gsl_stats_wvariance_with_fixed_mean"This function computes an unbiased estimate of the variance of the weighted dataset data when the population + mean of the underlying distribution is known with stride of the dataset and weights known and length of dataset is given " + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + input Real mean; + output Real wvariance; + external "C" wvariance=gsl_stats_wvariance_with_fixed_mean(w,wstride,data,stride,n,mean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wvariance_with_fixed_mean; + + function gsl_stats_wsd_with_fixed_mean"This function computes an unbiased estimate of the standard deviation of the weighted dataset data when the population + mean of the underlying distribution is known with stride of the dataset and weights known, length of dataset is given " + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + input Real mean; + output Real wsd; + external "C" wsd=gsl_stats_wsd_with_fixed_mean(w,wstride,data,stride,n,mean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wsd_with_fixed_mean; + + function gsl_stats_wtss"this function computes total sum of square about the weighted mean given weight array ,wstride,dataset array, stride,length" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + output Real wtss; + external "C" wtss=gsl_stats_wtss(w,wstride,data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wtss; + + + + function gsl_stats_wtss_m"this function computes total sum of square about the weighted mean given weight array ,wstride,dataset array, stride,length and weighted mean" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + input Real wmean; + output Real wtss; + external "C" wtss=gsl_stats_wtss_m(w,wstride,data,stride,n,wmean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wtss_m; + + function gsl_stats_wabsdev"This function calculates the weighted absolute deviation given the weight array,stride of the weights,the data set its stride and length of the dataset" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + output Real wabsdev; + external "C" wabsdev=gsl_stats_wabsdev(w,wstride,data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wabsdev; + + + function gsl_stats_wabsdev_m"this function computes weighted absolute deviation about the weighted mean given weight array ,wstride,dataset array, stride,length and weighted mean" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + input Real wmean; + output Real wabsdev; + external "C" wabsdev=gsl_stats_wabsdev_m(w,wstride,data,stride,n,wmean)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wabsdev_m; + + + + function gsl_stats_wskew"This function calculates the weighted skewness given the weight array,stride of the weights,the data set its stride and length of the dataset" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + output Real wskew; + external "C" wskew=gsl_stats_wskew(w,wstride,data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wskew; + + function gsl_stats_wskew_m_sd "this function computes weighted skewness about the weighted mean given weight array ,wstride,dataset array, stride,length , weighted mean and weighted standard deviation" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + input Real wmean; + input Real wsd; + output Real wskew; + external "C" wskew=gsl_stats_wskew_m_sd(w,wstride,data,stride,n,wmean,wsd)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wskew_m_sd; + + + function gsl_stats_wkurtosis"This function calculates the weighted kurtosis given the weight array,stride of the weights,the data set its stride and length of the dataset" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + output Real wkurtosis; + external "C" wkurtosis=gsl_stats_wkurtosis(w,wstride,data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wkurtosis; + + + function gsl_stats_wkurtosis_m_sd "this function computes weighted kurtosis about the weighted mean given weight array ,wstride,dataset array, stride,length , weighted mean and weighted standard deviation" + input Real w[:]; + input Integer wstride; + input Real data[:]; + input Integer stride; + input Integer n; + input Real wmean; + input Real wsd; + output Real wkurtosis; + external "C" wkurtosis=gsl_stats_wkurtosis_m_sd(w,wstride,data,stride,n,wmean,wsd)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_wkurtosis_m_sd; + + + + end chap_21_7; + + package chap_21_8"Maximum and Minimum of the dataset" + function gsl_stats_max"This function returns the maximum value in a given dataset with stride and length given" + input Real data[:]; + input Integer stride; + input Integer n; + output Real max; + external "C" max=gsl_stats_max(data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_max; + + + + function gsl_stats_min"This function returns the maximum value in a given dataset with stride and length given" + input Real data[:]; + input Integer stride; + input Integer n; + output Real min; + external "C" min=gsl_stats_min(data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_min; + + function gsl_stats_minmax"This function returns the minimum and maximum value in a given dataset with stride and length given" + output Real min; + output Real max; + input Real data[:]; + input Integer stride; + input Integer n; + external "C" gsl_stats_minmax(min,max,data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_minmax; + + + + + function gsl_stats_max_index"This function returns index of then maximum value in a given dataset with stride and length given" + input Real data[:]; + input Integer stride; + input Integer n; + output Real max_index; + external "C" max_index=gsl_stats_max_index(data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_max_index; + + + function gsl_stats_min_index"This function returns index of then maximum value in a given dataset with stride and length given" + input Real data[:]; + input Integer stride; + input Integer n; + output Real min_index; + external "C" min_index=gsl_stats_min_index(data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_min_index; + + + function gsl_stats_minmax_index"This function returns the index of minimum and maximum value in a given dataset with stride and length given" + + input Real data[:]; + input Real stride; + input Integer n; + output Real min_index; + output Real max_index; + external "C" gsl_stats_minmax_index(min_index,max_index,data,stride,n)annotation(Include="#include<gsl/gsl_statistics_double.h>",Library="gsl",Library="gslcblas"); + end gsl_stats_minmax_index; + + + end chap_21_8; + end STATISTICS; package Examples @@ -5185,14 +5623,507 @@ end gsl_sf_bessel_jl_e; package statistics package chap_21_1 model gsl_stats_mean - parameter Real data[:]={1,2,3,4,5}; + parameter Real data[:]={6.0,7.0,8.0,9.0,10.0}; parameter Integer stride=1; parameter Integer n=size(data,1); Real mean; algorithm - mean:=gsl.STATISTICS.chap_21_1.gsl_stats_mean(data[:],stride,n); + mean:=gsl.STATISTICS.chap_21_1.gsl_stats_mean(data,stride,n); end gsl_stats_mean; + + model gsl_stats_variance + "In this model we call the function gsl_stats_variance in gsl.STATISTICS.chap_21_1 to calculate variance of data set 'data'" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data,1); + Real variance; + algorithm + variance:=gsl.STATISTICS.chap_21_1.gsl_stats_variance(data,stride,n); + end gsl_stats_variance; + + model gsl_stats_variance_m "In this model we call the function gsl_stats_variance in gsl.STATISTICS.chap_21_1 to calculate Sampled variance of data set 'data' and given mean and stride " + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data,1); + parameter Real mean=2; + Real variance_m"Sampled variance"; + algorithm + variance_m:=gsl.STATISTICS.chap_21_1.gsl_stats_variance_m(data,stride,n,mean); + end gsl_stats_variance_m; + + model gsl_stats_sd + "In this model we call the function gsl_stats_sd in gsl.STATISTICS.chap_21_1 to calculate Standard deviation of data set 'data'" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data,1); + Real sd; + algorithm + sd:=gsl.STATISTICS.chap_21_1.gsl_stats_sd(data,stride,n); + end gsl_stats_sd; + + model gsl_stats_sd_m "In this model we call the function gsl_stats_sd_m in gsl.STATISTICS.chap_21_1 to calculate Sampled standard deviation of data set 'data' and given mean and stride " + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data,1); + parameter Real mean=2; + Real sd_m"Sampled standard deviation"; + algorithm + sd_m:=gsl.STATISTICS.chap_21_1.gsl_stats_sd_m(data,stride,n,mean); + end gsl_stats_sd_m; + + model gsl_stats_tss + "In this model we call the function gsl_stats_tss in gsl.STATISTICS.chap_21_1 to calculate total sum of squares of data set about mean" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data,1); + Real tss"total sum of squares"; + algorithm + tss:=gsl.STATISTICS.chap_21_1.gsl_stats_tss(data,stride,n); + end gsl_stats_tss; + + model gsl_stats_tss_m " This model calls the function gsl_stats_m to calculate Sampled total sum of squares of data set 'data' and given mean and stride " + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data,1); + parameter Real mean=2; + Real tss_m"total sum of squares"; + algorithm + tss_m:=gsl.STATISTICS.chap_21_1.gsl_stats_tss_m(data,stride,n,mean); + end gsl_stats_tss_m; + + model gsl_stats_variance_with_fixed_mean"This model calls the function gsl_stats_variance_with_fixed_mean to calculate the variance with the mean known a priori" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data,1); + parameter Real mean=2; + Real variance"Sampled variance"; + algorithm + variance:=gsl.STATISTICS.chap_21_1.gsl_stats_variance_with_fixed_mean(data,stride,n,mean); + end gsl_stats_variance_with_fixed_mean; + + model gsl_stats_sd_with_fixed_mean"This model calls the function gsl_stats_sd_with_fixed_mean to calculate the standard deviation with the mean known a priori" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data,1); + parameter Real mean=2; + Real sd"Standard Deviation"; + algorithm + sd:=gsl.STATISTICS.chap_21_1.gsl_stats_sd_with_fixed_mean(data,stride,n,mean); + end gsl_stats_sd_with_fixed_mean; + + end chap_21_1; + + package chap_21_2 + model gsl_stats_absdev"This model calls the function gsl_stats_absdev to calculate the absolute deviation of the given data set ,stride and length" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=1; + parameter Integer n=size(data, 1); + Real absdev; + algorithm + absdev:=gsl.STATISTICS.chap_21_2.gsl_stats_absdev(data,stride,n); + end gsl_stats_absdev; + + model gsl_stats_absdev_m"This model calls the function + gsl_stats_absdev_m to calculate absolute deviation with given mean ,data set,stride and length" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=1; + parameter Integer n=size(data, 1); + parameter Real mean=2; + Real absdev_m; + algorithm + absdev_m:=gsl.STATISTICS.chap_21_2.gsl_stats_absdev_m(data,stride,n,mean); + end gsl_stats_absdev_m; + + end chap_21_2; + package chap_21_3 + model gsl_stats_skew"model calls the function + gsl_stats_skew to calculate skewness with given data set,stride and length" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=1; + parameter Integer n=size(data, 1); + Real skew; + algorithm + skew:=gsl.STATISTICS.chap_21_3.gsl_stats_skew(data,stride,n); + end gsl_stats_skew; + + model gsl_stats_skew_m_sd"This model calls the function gsl_stats_skew_m_sd to calculate the skewness given the dataset,stride,length,mean and standard deviation" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data, 1); + parameter Real mean=1.8; + parameter Real sd=2.16795; + Real skew; + algorithm + skew:=gsl.STATISTICS.chap_21_3.gsl_stats_skew_m_sd(data,stride,n,mean,sd); + end gsl_stats_skew_m_sd; + + + model gsl_stats_kurtosis"model calls the function + gsl_stats_kurtosis to calculate kurtosis with given data set,stride and length" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data, 1); + Real kurtosis; + algorithm + kurtosis:=gsl.STATISTICS.chap_21_3.gsl_stats_kurtosis(data,stride,n); + end gsl_stats_kurtosis; + + + model gsl_stats_kurtosis_m_sd"This model calls the function gsl_stats_kurtosis_m_sd to calculate the kurtosis given the dataset,stride,length,mean and standard deviation" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data, 1); + parameter Real mean=1.8; + parameter Real sd=2.16795; + Real kurtosis; + algorithm + kurtosis:=gsl.STATISTICS.chap_21_3.gsl_stats_skew_m_sd(data,stride,n,mean,sd); + end gsl_stats_kurtosis_m_sd; + end chap_21_3; + package chap_21_4 + model gsl_stats_lag1_autocorrelation + "In this model we call the function gsl_stats_lag1_autocorrelationin to calculate lag1 autocorrelation of data set 'data'" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=2; + parameter Integer n=size(data,1); + Real lag1; + algorithm + lag1:=gsl.STATISTICS.chap_21_4.gsl_stats_lag1_autocorrelation(data,stride,n); + end gsl_stats_lag1_autocorrelation; + + + + + + + + + + + model gsl_stats_lag1_autocorrelation_m + "In this model we call the function gsl_stats_lag1_autocorrelationin_m to calculate lag1 autocorrelation of data set 'data'given stride,length and mean" + parameter Real data[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride=1; + parameter Integer n=size(data,1); + parameter Real mean=1.8; + Real lag1; + algorithm + lag1:=gsl.STATISTICS.chap_21_4.gsl_stats_lag1_autocorrelation_m(data,stride,n,mean); + end gsl_stats_lag1_autocorrelation_m; + end chap_21_4; + package chap_21_5 + model gsl_stats_covariance"This model calls the function gsl_stats_covariance to compute the covariance of the data-sets data1 and data2 ,stride1 and stride2 and length n" + parameter Real data1[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride1=1; + parameter Real data2[:]={6.0,7.0,8.0,9.0,10.0}; + parameter Integer stride2=1; + parameter Integer n=5; + Real covar; + algorithm + covar:=gsl.STATISTICS.chap_21_5.gsl_stats_covariance(data1,stride1,data2,stride2,n); + end gsl_stats_covariance; + + model gsl_stats_covariance_m"This model calls the function gsl_stats_covariance_m to compute the covariance of the data-sets data1 and data2 ,stride1 and stride2 and length n and mean1 and mean2" + parameter Real data1[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride1=1; + parameter Real data2[:]={6.0,7.0,8.0,9.0,10.0}; + parameter Integer stride2=1; + parameter Integer n=5; + parameter Real mean1=3; + parameter Real mean2=8; + Real covar; + algorithm + covar:=gsl.STATISTICS.chap_21_5.gsl_stats_covariance_m(data1,stride1,data2,stride2,n,mean1,mean2); + end gsl_stats_covariance_m; + + end chap_21_5; + package chap_21_6 + model gsl_stats_correlation"This model calls the function gsl_stats_correlation to compute the Pearson correlation coefficient of the data-sets data1 and data2 ,stride1 and stride2 and length n" + parameter Real data1[:]={1.0,2.0,3.0,4.0,5.0}; + parameter Integer stride1=1; + parameter Real data2[:]={6.0,7.0,8.0,9.0,10.0}; + parameter Integer stride2=1; + parameter Integer n=5; + Real r; + algorithm + r:=gsl.STATISTICS.chap_21_6.gsl_stats_correlation(data1,stride1,data2,stride2,n); + end gsl_stats_correlation; + + model gsl_stats_spearman"This model calls the function gsl_stats_spearman to calculate the spearmans coefficient" + parameter Real data1[:]={1.0,2.0,3.0,5.0,4.0}; + parameter Integer stride1=1; + parameter Real data2[:]={6.0,7.0,8.0,9.0,1.0}; + parameter Integer stride2=1; + parameter Integer n=5; + Real work[2*n]; + Real y; + algorithm + (work,y):=gsl.STATISTICS.chap_21_6.gsl_stats_spearman(data1,stride1,data2,stride2,n); + end gsl_stats_spearman; + end chap_21_6; + package chap_21_7 + model gsl_stats_wmean + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + Real wmean; + algorithm + (wmean):=gsl.STATISTICS.chap_21_7.gsl_stats_wmean(w,wstride,data,stride,n); + end gsl_stats_wmean; + + + + + + + model gsl_stats_wvariance"This model calls the function gsl_stats_wvariance to calculate the weighted variance given the weights ,stride of weights,data, stride of data ,length of the data" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + Real wvariance; + algorithm + wvariance:=gsl.STATISTICS.chap_21_7.gsl_stats_wvariance(w,wstride,data,stride,n); + end gsl_stats_wvariance; + + + model gsl_stats_wvariance_m"This model calls the function gsl_stats_wvariance to calculate the weighted variance given the weights ,stride of weights,data, stride of data ,length of the data and weighted mean" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + parameter Real wmean=1; + Real wvariance; + algorithm + wvariance:=gsl.STATISTICS.chap_21_7.gsl_stats_wvariance_m(w,wstride,data,stride,n,wmean); + end gsl_stats_wvariance_m; + + model gsl_stats_wsd"This model calls the function gsl_stats_wsd to calculate the weighted standard deviation given the weights ,stride of weights,data, stride of data ,length of the data" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + Real wsd; + algorithm + wsd:=gsl.STATISTICS.chap_21_7.gsl_stats_wsd(w,wstride,data,stride,n); + end gsl_stats_wsd; + + + model gsl_stats_wsd_m"This model calls the function gsl_stats_wsd_m to calculate the weighted standard deviation given the weights ,stride of weights,data, stride of data ,length of the data and weighted mean" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + parameter Real wmean=1; + Real wsd; + algorithm + wsd:=gsl.STATISTICS.chap_21_7.gsl_stats_wsd_m(w,wstride,data,stride,n,wmean); + end gsl_stats_wsd_m; + + + + model gsl_stats_wvariance_with_fixed_mean"This model calls the function gsl_stats_wvariance_with_fixed_mean to calculate the weighted variance given the weights ,stride of weights,data, stride of data ,length of the data and population mean" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + parameter Real mean=1; + Real wvariance; + algorithm + wvariance:=gsl.STATISTICS.chap_21_7.gsl_stats_wvariance_with_fixed_mean(w,wstride,data,stride,n,mean); + end gsl_stats_wvariance_with_fixed_mean; + + + + + + model gsl_stats_wsd_with_fixed_mean"This model calls the function gsl_stats_wsd_with_fixed_mean to calculate the weighted variance given the weights ,stride of weights,data, stride of data ,length of the data and population mean" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + parameter Real mean=1; + Real wsd; + algorithm + wsd:=gsl.STATISTICS.chap_21_7.gsl_stats_wsd_with_fixed_mean(w,wstride,data,stride,n,mean); + end gsl_stats_wsd_with_fixed_mean; + + + model gsl_stats_wtss"This model calls the function gsl_stats_wtss to calculate the total sum of squares about weighted mean given the weights ,stride of weights,data, stride of data ,length of the data" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + Real wtss; + algorithm + wtss:=gsl.STATISTICS.chap_21_7.gsl_stats_wtss(w,wstride,data,stride,n); + end gsl_stats_wtss; + + + + model gsl_stats_wtss_m"This model calls the function gsl_stats_wtss_m to calculate the total sum of squares about weighted mean given the weights ,stride of weights,data, stride of data ,length of the data,weighted mean 'wmean'" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + parameter Real wmean=1; + Real wtss; + algorithm + wtss:=gsl.STATISTICS.chap_21_7.gsl_stats_wtss_m(w,wstride,data,stride,n,wmean); + end gsl_stats_wtss_m; + + + model gsl_stats_wabsdev"This model calls the function gsl_stats_wabsdev to calculate the weighted absolute deviation given the weights ,stride of weights,data, stride of data ,length of the data" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + Real wabsdev; + algorithm + wabsdev:=gsl.STATISTICS.chap_21_7.gsl_stats_wabsdev(w,wstride,data,stride,n); + end gsl_stats_wabsdev; + + model gsl_stats_wabsdev_m"This model calls the function gsl_stats_wabsdev_m to calculate the weighted absolute deviation about weighted mean given the weights ,stride of weights,data, stride of data ,length of the data,weighted mean 'wmean'" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + parameter Real wmean=1; + Real wabsdev; + algorithm + wabsdev:=gsl.STATISTICS.chap_21_7.gsl_stats_wabsdev_m(w,wstride,data,stride,n,wmean); + end gsl_stats_wabsdev_m; + + model gsl_stats_wskew"This model calls the function gsl_stats_wskew to calculate the weighted skewness given the weights ,stride of weights,data, stride of data ,length of the data" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + Real wskew; + algorithm + wskew:=gsl.STATISTICS.chap_21_7.gsl_stats_wskew(w,wstride,data,stride,n); + end gsl_stats_wskew; + + + + model gsl_stats_wskew_m_sd"This model calls the function gsl_stats_wskew_m_sd to calculate the skewness about weighted mean given the weights ,stride of weights,data, stride of data ,length of the data,weighted mean 'wmean',weighted standard deviation" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + parameter Real wmean=1; + parameter Real wsd=0; + Real wskew; + algorithm + wskew:=gsl.STATISTICS.chap_21_7.gsl_stats_wskew_m_sd(w,wstride,data,stride,n,wmean,wsd); + end gsl_stats_wskew_m_sd; + + + model gsl_stats_wkurtosis"This model calls the function gsl_stats_wkurtosis to calculate the weighted kurtosis given the weights ,stride of weights,data, stride of data ,length of the data" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + Real wkurtosis; + algorithm + wkurtosis:=gsl.STATISTICS.chap_21_7.gsl_stats_wkurtosis(w,wstride,data,stride,n); + end gsl_stats_wkurtosis; + + model gsl_stats_wkurtosis_m_sd"This model calls the function gsl_stats_wkurtosis_m_sd to calculate the weighted kurtosis about weighted mean given the weights ,stride of weights,data, stride of data ,length of the data,weighted mean 'wmean',weighted standard deviation" + parameter Real w[:]={0.2,0.2,0.2,0.2,0.2} ; + parameter Integer wstride=1; + parameter Real data[:]={1.0,1.0,1.0,1.0,1.0}; + parameter Integer stride=1; + parameter Integer n=5; + parameter Real wmean=1; + parameter Real wsd=0; + Real wkurtosis; + algorithm + wkurtosis:=gsl.STATISTICS.chap_21_7.gsl_stats_wkurtosis_m_sd(w,wstride,data,stride,n,wmean,wsd); + end gsl_stats_wkurtosis_m_sd; + + end chap_21_7; + + package chap_21_8 + model gsl_stats_max"This model calls the function gsl_stats_max to return the maximum of the given dataset" + parameter Real data[:]={6.0,7.0,8.0,9.0,10.0}; + parameter Integer stride=1; + parameter Integer n=size(data,1); + Real max; + algorithm + max:=gsl.STATISTICS.chap_21_8.gsl_stats_max(data,stride,n); + end gsl_stats_max; + + + model gsl_stats_min"This model calls the function gsl_stats_min to return the maximum of the given dataset" + parameter Real data[:]={6.0,7.0,8.0,9.0,10.0}; + parameter Integer stride=1; + parameter Integer n=size(data,1); + Real min; + algorithm + min:=gsl.STATISTICS.chap_21_8.gsl_stats_min(data,stride,n); + end gsl_stats_min; + + + model gsl_stats_minmax"This model calls the function gsl_stats_minmax to return the maximum of the given dataset" + Real min; + Real max; + parameter Real data[:]={6.0,7.0,8.0,9.0,10.0}; + parameter Integer stride=1; + parameter Integer n=size(data,1); + algorithm + (min,max):=gsl.STATISTICS.chap_21_8.gsl_stats_minmax(data,stride,n); + end gsl_stats_minmax; + + + model gsl_stats_max_index"This model calls the function gsl_stats_max_index to return index of then maximum value of the given dataset" + parameter Real data[:]={6.0,7.0,8.0,9.0,10.0}; + parameter Integer stride=1; + parameter Integer n=size(data,1); + Real max_index; + algorithm + max_index:=gsl.STATISTICS.chap_21_8.gsl_stats_max_index(data,stride,n)+1; + end gsl_stats_max_index; + + model gsl_stats_min_index"This model calls the function gsl_stats_min_index to return index of then minimum value of the given dataset" + parameter Real data[:]={6.0,7.0,8.0,9.0,10.0}; + parameter Integer stride=1; + parameter Integer n=size(data,1); + Real min_index; + algorithm + min_index:=gsl.STATISTICS.chap_21_8.gsl_stats_min_index(data,stride,n)+1; + end gsl_stats_min_index; + + + model gsl_stats_minmax_index"This model calls the function gsl_stats_minmax_index to return the index of maximum and minimum of the given dataset" + + parameter Real data[:]={100.0,7.0,8.0,9.0,10.0}; + parameter Real stride=1; + parameter Integer n=size(data,1); + Real min_index; + Real max_index; + algorithm + (min_index,max_index):=gsl.STATISTICS.chap_21_8.gsl_stats_minmax_index(data,stride,n); + end gsl_stats_minmax_index; + end chap_21_8; + + end statistics; end Examples; end gsl;
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