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| -rw-r--r-- | External_Functions/gsl.mo | 497 |
1 files changed, 496 insertions, 1 deletions
diff --git a/External_Functions/gsl.mo b/External_Functions/gsl.mo index 169fd75..4a7bef0 100644 --- a/External_Functions/gsl.mo +++ b/External_Functions/gsl.mo @@ -3543,6 +3543,348 @@ end gsl_sf_bessel_jl_e; end chap_21_8; end STATISTICS; +package chap_21_9 + function gsl_stats_median_from_sorted_data "given the sorted dataset ,stride and the length the function computes the median" + input Real sorted_data[:]; + input Integer stride; + input Integer n; + output Real median; + + external "C" median = gsl_stats_median_from_sorted_data(sorted_data, stride, n) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_median_from_sorted_data; + + function gsl_stats_median "This function computes the median given dataset,stride and length" + input Real data[:]; + input Integer stride; + input Integer n; + output Real median; + + external "C" median = gsl_stats_median(data, stride, n) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_median; + + function gsl_stats_quantile_from_sorted_data "given the sorted dataset ,stride and the length and the function computes the quantile" + input Real sorted_data[:]; + input Integer stride; + input Integer n; + input Real f "percentile/100"; + output Real quantile; + + external "C" quantile = gsl_stats_quantile_from_sorted_data(sorted_data, stride, n, f) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_quantile_from_sorted_data; + end chap_21_9; + + package chap_21_10 + function gsl_stats_select "This function returns the kth order statistics (kth smallest value) of a given dataset" + input Real data[:]; + input Integer stride; + input Integer n; + input Integer k; + output Real d; + + external "C" d = gsl_stats_select(data, stride, n, k) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_select; + end chap_21_10; + + package chap_21_11 "Robust location estimates " + function gsl_stats_trmean_from_sorted_data "This function returns the trimmed mean given sorted data set, trimming factor, stride and length" + input Real alpha; + input Real sorted_data[:]; + input Integer stride; + input Integer n; + output Real trmean; + + external "C" trmean = gsl_stats_trmean_from_sorted_data(alpha, sorted_data, stride, n) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_trmean_from_sorted_data; + + function gsl_stats_gastwirth_from_sorted_data "gastwirth=(0.3*Q1/2)+(0.4*Q1/2)+(0.3*Q2/3) where Q1/3 is one third quantile,Q1/2 is half quantile and Q2/3 is two third quantile" + input Real sorted_data[:]; + input Integer stride; + input Integer n; + output Real gastwirth; + + external "C" gastwirth = gsl_stats_gastwirth_from_sorted_data(sorted_data, stride, n) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_gastwirth_from_sorted_data; + end chap_21_11; + + package chap_21_12_1 + function gsl_stats_mad0 "The function calculates median absolute deviation(MAD),where MAD0=median{|xi- median(x)|} given dataset ,stride,length and a workspace of size n" + input Real data[:]; + input Integer stride; + input Integer n; + output Real work[n]; + output Real mad0; + + external "C" mad0 = gsl_stats_mad0(data, stride, n, work) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_mad0; + + function gsl_stats_mad "The function calculates median absolute deviation(MAD),where MAD=1.4826*median{|xi- median(x)|} given dataset ,stride,length and a workspace of size n" + input Real data[:]; + input Integer stride; + input Integer n; + output Real work[n]; + output Real mad; + + external "C" mad = gsl_stats_mad(data, stride, n, work) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_mad; + end chap_21_12_1; + + package chap_21_12_2 "Sn Statistic" + function gsl_stats_Sn0_from_sorted_data "This function returns the statistics given sorted data set, workspace of length n , stride and length.where S n = c n × median i {median j (|x i − x j |)}" + input Real sorted_data[:]; + input Integer stride; + input Integer n; + output Real work[n]; + output Real Sn0; + + external "C" Sn0 = gsl_stats_Sn0_from_sorted_data(sorted_data, stride, n, work) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_Sn0_from_sorted_data; + + function gsl_stats_Sn_from_sorted_data "This function returns the statistics given sorted data set, workspace of length n , stride and length.where S n =1.1926× cn × median i {median j (|x i − x j |)}" + input Real sorted_data[:]; + input Integer stride; + input Integer n; + output Real work[n]; + output Real Sn; + + external "C" Sn = gsl_stats_Sn_from_sorted_data(sorted_data, stride, n, work) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_Sn_from_sorted_data; + end chap_21_12_2; + + package chap_21_12_3 + function gsl_stats_Qn0_from_sorted_data "This function returns the statistics given sorted data set, workspace of length n , stride and length.where Q n 0= d n × {|x i − x j | , i < j} (k)" + input Real sorted_data[:]; + input Integer stride; + input Integer n; + output Real work[3 * n]; + output Integer work_int[5 * n]; + output Real Qn0; + + external "C" Qn0 = gsl_stats_Qn0_from_sorted_data(sorted_data, stride, n, work, work_int) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_Qn0_from_sorted_data; + + function gsl_stats_Qn_from_sorted_data "This function returns the statistics given sorted data set, workspace of length n , stride and length.where Q n = 2.21914 × d n × {|x i − x j | , i < j} (k)" + input Real sorted_data[:]; + input Integer stride; + input Integer n; + output Real work[3 * n]; + output Integer work_int[5 * n]; + output Real Qn; + + external "C" Qn = gsl_stats_Qn_from_sorted_data(sorted_data, stride, n, work, work_int) annotation( + Include = "#include<gsl/gsl_statistics_double.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_stats_Qn_from_sorted_data; + end chap_21_12_3; + end STATISTICS; + + package RUNNING_STATISTICS + package chap_22_2 + function gsl_rstat_reset "resets the accumulator so that it can be used for other data" + input gsl.data_types.gsl_rstat_workspace p; + output Integer n; + + external "C" n = gsl_rstat_reset(p) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_reset; + + function gsl_rstat_add "adds data to the accumulator" + input Real x; + input gsl.data_types.gsl_rstat_workspace S; + output Integer i; + + external "C" i = gsl_rstat_add(x, S) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_add; + + function gsl_rstat_n "returns the number of data accumulated in the accumulator" + input gsl.data_types.gsl_rstat_workspace w; + output Integer n; + + external "C" n = gsl_rstat_n(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_n; + end chap_22_2; + + package chap_22_3 + function gsl_rstat_min "returns the minimum value added to the accumulator" + input gsl.data_types.gsl_rstat_workspace w; + output Real n; + + external "C" n = gsl_rstat_min(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_min; + + function gsl_rstat_max "returns the minimum value added to the accumulator" + input gsl.data_types.gsl_rstat_workspace w; + output Real n; + + external "C" n = gsl_rstat_max(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_max; + + function gsl_rstat_mean "returns the maximum value added to the accumulator" + input gsl.data_types.gsl_rstat_workspace w; + output Real n; + + external "C" n = gsl_rstat_mean(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_mean; + + function gsl_rstat_variance "returns the variance of the accumulator data" + input gsl.data_types.gsl_rstat_workspace w; + output Real n; + + external "C" n = gsl_rstat_variance(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_variance; + + function gsl_rstat_sd "returns the standard deviation of accumulator" + input gsl.data_types.gsl_rstat_workspace w; + output Real n; + + external "C" n = gsl_rstat_sd(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_sd; + + function gsl_rstat_sd_mean "This function returns the standard deviation of the mean," + input gsl.data_types.gsl_rstat_workspace w; + output Real n; + + external "C" n = gsl_rstat_sd_mean(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_sd_mean; + + function gsl_rstat_rms "This function returns the root mean square of all data added to the accumulator" + input gsl.data_types.gsl_rstat_workspace w; + output Real n; + + external "C" n = gsl_rstat_rms(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_rms; + + function gsl_rstat_skew "This function returns the skewness of all data added to the accumulator" + input gsl.data_types.gsl_rstat_workspace w; + output Real n; + + external "C" n = gsl_rstat_skew(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_skew; + + function gsl_rstat_kurtosis "This function returns the kurtosis of all data added to the accumulator" + input gsl.data_types.gsl_rstat_workspace w; + output Real n; + + external "C" n = gsl_rstat_kurtosis(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_kurtosis; + + function gsl_rstat_median "This function returns an estimate of the median of the data added to the accumulator" + input gsl.data_types.gsl_rstat_workspace w; + output Real n; + + external "C" n = gsl_rstat_median(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_median; + end chap_22_3; + + package chap_22_4 + function gsl_rstat_quantile_reset "resets the accumulator so that it can be used for other data" + input gsl.data_types.gsl_rstat_quantile_workspace w; + output Integer n; + + external "C" n = gsl_rstat_quantile_reset(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_quantile_reset; + + function gsl_rstat_quantile_add "adds data to the accumulator" + input Real x; + input gsl.data_types.gsl_rstat_quantile_workspace S; + output Integer i; + + external "C" i = gsl_rstat_quantile_add(x, S) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_quantile_add; + + function gsl_rstat_quantile_get "returns the number of data accumulated in the accumulator" + input gsl.data_types.gsl_rstat_quantile_workspace w; + output Integer n; + + external "C" n = gsl_rstat_quantile_get(w) annotation( + Include = "#include<gsl/gsl_rstat.h>", + Library = "gsl", + Library = "gslcblas"); + end gsl_rstat_quantile_get; + end chap_22_4; + end RUNNING_STATISTICS; + + package MOVING_WINDOW_STATISTICS + end MOVING_WINDOW_STATISTICS; + + package Examples package Mathematical @@ -6125,5 +6467,158 @@ end gsl_sf_bessel_jl_e; end statistics; +package chap_21_9 "Median and Percentiles" + model gsl_stats_median_from_sorted_data "This model calls the function 'gsl_stats_median_from_sorted_data' to calculate the median . The given dataset should be in asending order" + parameter Real sorted_data[:] = {1, 2, 4, 5, 7, 9}; + parameter Integer stride = 1; + parameter Integer n = size(sorted_data, 1); + Real median; + algorithm + median := gsl.STATISTICS.chap_21_9.gsl_stats_median_from_sorted_data(sorted_data, stride, n); + end gsl_stats_median_from_sorted_data; + + model gsl_stats_median "This model calls the function 'gsl_stats_median' to calculate the median" + parameter Real data[:] = {1, 2, 4, 5, 7, 9}; + parameter Integer stride = 1; + parameter Integer n = size(data, 1); + Real median; + algorithm + median := gsl.STATISTICS.chap_21_9.gsl_stats_median(data, stride, n); + end gsl_stats_median; + + model gsl_stats_quantile_from_sorted_data "This model calls the function 'gsl_stats_quantile_from_sorted_data' to calculate the quantile . The given dataset should be in asending order" + parameter Real sorted_data[:] = {1, 2, 4, 5, 7, 9}; + parameter Integer stride = 1; + parameter Integer n = size(sorted_data, 1); + parameter Real f = 0.75 "percentile/100"; + Real quantile; + algorithm + quantile := gsl.STATISTICS.chap_21_9.gsl_stats_quantile_from_sorted_data(sorted_data, stride, n, f); + end gsl_stats_quantile_from_sorted_data; + end chap_21_9; + + package chap_21_10 + model gsl_stats_select "This model calls the function gsl_stats_select to give the kth smallest value of the given dataset" + Real data[:] = {4, 2, 3, 1, 1000, 10, 9}; + parameter Integer stride = 1; + parameter Integer n = size(data, 1); + parameter Integer k = 5; + Real d; + algorithm + d := gsl.STATISTICS.chap_21_10.gsl_stats_select(data, stride, n, k - 1); + end gsl_stats_select; + end chap_21_10; + + package chap_21_11 + model gsl_stats_trmean_from_sorted_data "This model calls the function 'gsl_stats_trmean_from_sorted_data' to give the trimmed mean given sorted dataset trimming factor 'alpha',stride,length" + parameter Real alpha = 0.25; + parameter Real sorted_data[:] = {1, 2, 3, 4, 5, 6, 7, 8}; + parameter Integer stride = 1; + parameter Integer n = size(sorted_data, 1); + Real trmean; + algorithm + trmean := gsl.STATISTICS.chap_21_11.gsl_stats_trmean_from_sorted_data(alpha, sorted_data, stride, n); + end gsl_stats_trmean_from_sorted_data; + + model gsl_stats_gastwirth_from_sorted_data "This model calls the function 'gsl_stats_gastwirth_from_sorted_data' to give gastwirth location estimator given sorted data,stride ,length" + parameter Real sorted_data[:] = {1000, 2, 3, 1000, 5, 1000, 7, 1000}; + parameter Integer stride = 1; + parameter Integer n = size(sorted_data, 1); + Real gastwirth; + algorithm + gastwirth := gsl.STATISTICS.chap_21_11.gsl_stats_gastwirth_from_sorted_data(sorted_data, stride, n); + end gsl_stats_gastwirth_from_sorted_data; + end chap_21_11; + + package chap_21_12_1 + model gsl_stats_mad0 "This model calls the function gsl_stats_mad0 to calculate median absolute deviation(MAD) without the correction factor " + parameter Real data[:] = {1, 34, 6, 5, 9}; + parameter Integer stride = 1; + parameter Integer n = size(data, 1); + Real work[size(data, 1)]; + Real mad0; + algorithm + (work, mad0) := gsl.STATISTICS.chap_21_12_1.gsl_stats_mad0(data, stride, n); + end gsl_stats_mad0; + + model gsl_stats_mad "This model calls the function gsl_stats_mad to calculate median absolute deviation(MAD) " + parameter Real data[:] = {1, 34, 6, 5, 9}; + parameter Integer stride = 1; + parameter Integer n = size(data, 1); + Real work[size(data, 1)]; + Real mad; + algorithm + (work, mad) := gsl.STATISTICS.chap_21_12_1.gsl_stats_mad(data, stride, n); + end gsl_stats_mad; + end chap_21_12_1; + + package chap_21_12_2 "Sn Statistic" + model gsl_stats_Sn0_from_sorted_data "This model calls the function 'gsl_stats_Sn0_from_sorted_data' to give the Statistics given sorted dataset , workspace of length n,stride,length" + parameter Real sorted_data[:] = {1, 2, 3, 4, 5, 6, 7, 8}; + parameter Integer stride = 1; + parameter Integer n = size(sorted_data, 1); + Real work[size(sorted_data, 1)]; + Real Sn0; + algorithm + (work, Sn0) := gsl.STATISTICS.chap_21_12_2.gsl_stats_Sn0_from_sorted_data(sorted_data, stride, n); + end gsl_stats_Sn0_from_sorted_data; + + model gsl_stats_Sn_from_sorted_data "This model calls the function 'gsl_stats_Sn_from_sorted_data' to give the Statistics given sorted dataset , workspace of length n,stride,length" + parameter Real sorted_data[:] = {1, 2, 3, 4, 5, 6, 7, 8}; + parameter Integer stride = 1; + parameter Integer n = size(sorted_data, 1); + Real work[size(sorted_data, 1)]; + Real Sn; + algorithm + (work, Sn) := gsl.STATISTICS.chap_21_12_2.gsl_stats_Sn_from_sorted_data(sorted_data, stride, n); + end gsl_stats_Sn_from_sorted_data; + end chap_21_12_2; + + package chap_21_12_3 + model gsl_stats_Qn0_from_sorted_data "This model calls the function 'gsl_stats_Qn0_from_sorted_data' to give the Statistics given sorted dataset , workspace of length n,stride,length" + parameter Real sorted_data[:] = {1, 2, 3, 4, 5, 6, 7, 8}; + parameter Integer stride = 1; + parameter Integer n = size(sorted_data, 1); + Real work[3 * size(sorted_data, 1)]; + Integer work_int[size(sorted_data, 1) * 5]; + Real Qn0; + algorithm + (work, work_int, Qn0) := gsl.STATISTICS.chap_21_12_3.gsl_stats_Qn0_from_sorted_data(sorted_data, stride, n); + end gsl_stats_Qn0_from_sorted_data; + + model gsl_stats_Qn_from_sorted_data "This model calls the function 'gsl_stats_Qn_from_sorted_data' to give the Statistics given sorted dataset , workspace of length n,stride,length,workspace for integer " + parameter Real sorted_data[:] = {1, 2, 3, 4, 5, 6, 7, 8}; + parameter Integer stride = 1; + parameter Integer n = size(sorted_data, 1); + Real work[3 * size(sorted_data, 1)]; + Integer work_int[5 * size(sorted_data, 1)]; + Real Qn; + algorithm + (work, work_int, Qn) := gsl.STATISTICS.chap_21_12_3.gsl_stats_Qn_from_sorted_data(sorted_data, stride, n); + end gsl_stats_Qn_from_sorted_data; + end chap_21_12_3; + end statistics; + + package running_statistics + model test + gsl.data_types.gsl_rstat_workspace rstat_p = gsl.data_types.gsl_rstat_workspace(); + parameter Real data[:] = {17.2, 18.1}; + Real mean; + //, 16.5, 18.3, 12.6}; + /* Real mean, variance, largest, smallest, sd, rms, sd_mean, median, skew, kurtosis;*/ + //Real i; + Real j[2]; + algorithm +/* add data to rstat accumulator */ +//for i in 1:2 loop + j[1] := gsl.RUNNING_STATISTICS.chap_22_2.gsl_rstat_add(data[1], rstat_p); + j[2] := gsl.RUNNING_STATISTICS.chap_22_2.gsl_rstat_add(data[2], rstat_p); +//end for; + mean := gsl.RUNNING_STATISTICS.chap_22_3.gsl_rstat_mean(rstat_p); +//end for; +// i := 5; + end test; + end running_statistics; + end Examples; -end gsl;
\ No newline at end of file +end gsl; |