«Письмо турецкому султану» или линейная регрессия на C# с помощью Accord.NET для анализа открытых данных Москвы

• «Ловись Data большая и маленькая!» — (Краткий обзор курсов по Data Science от Cognitive Class)
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• ««Паровозик, который смог!» или «Специализация Машинное обучение и анализ данных», глазами новичка в Data Science

• “Восстание МашинLearning” или совмещаем хобби по Data Science и анализу спектров лампочек
• «Как по нотам!» или Машинное обучение (Data science) на C# с помощью Accord.NET Framework
• «Используй Силу машинного обучения, Люк!» или автоматическая классификация светильников по КСС
• «4 свадьбы и одни похороны» или линейная регрессия для анализа открытых данных правительства Москвы

using System;
using System.Linq;
using Accord.Statistics.Models.Regression.Linear;
using Accord.IO;
using Accord.Math;
using System.Data;
using System.Collections.Generic;
using Accord.Controls;
using Accord.Math.Optimization.Losses;

namespace cs_msc_mayor
{
    class Program
    {
       
        static void Main(string[] args)
        {
            //for separating the training and test samples
            int traintPos = 18;
            int testPos = 22;
            int allData = testPos + (testPos - traintPos);


            //for correct reading symbol of float point in csv
            System.Globalization.CultureInfo customCulture = (System.Globalization.CultureInfo)System.Threading.Thread.CurrentThread.CurrentCulture.Clone();
            customCulture.NumberFormat.NumberDecimalSeparator = ".";
            System.Threading.Thread.CurrentThread.CurrentCulture = customCulture;


            //read data
            string CsvFilePath = @"msc_appel_data.csv";
            DataTable mscTable = new CsvReader(CsvFilePath, true).ToTable();

            //for encoding the string values of months into numerical values
            Dictionary<string, double> monthNames = new Dictionary<string, double>
            {
                ["January"] = 1,
                ["February"] = 2,
                ["March"] = 3,
                ["April"] = 4,
                ["May"] = 5,
                ["June"] = 6,
                ["July"] = 7,
                ["August"] = 8,
                ["September"] = 9,
                ["October"] = 10,
                ["November"] = 11,
                ["December"] = 12

            };


            string[] months = mscTable.Columns["month"].ToArray<String>();
            double[] dMonths= new double[months.Length];

            for (int i=0; i< months.Length; i++)
            {
                dMonths[i] = monthNames[months[i]];
                //Console.WriteLine(dMonths[i]);
            }

            //select the target column
            double[] OutResPositive = mscTable.Columns["res_positive"].ToArray();

            // separation of the test and train target sample
            double[] OutResPositiveTrain = OutResPositive.Get(0, traintPos);
            double[] OutResPositiveTest = OutResPositive.Get(traintPos, testPos);

            //deleting unneeded columns
            mscTable.Columns.Remove("total_appeals");
            mscTable.Columns.Remove("month");
            mscTable.Columns.Remove("res_positive");
            mscTable.Columns.Remove("year");


            //add coded in a double column month into Table

            //create new column
            DataColumn newCol = new DataColumn("dMonth", typeof(double));
            newCol.AllowDBNull = true;
            // add new column
            mscTable.Columns.Add(newCol);

            //fill new column
            int counter = 0;
            foreach (DataRow row in mscTable.Rows)
            {
                row["dMonth"] = dMonths[counter];
                counter++;
            }


            //receiving input data from a table
            double[][] inputs = mscTable.ToArray();

            

            //separation of the test and train sample
            double[][] inputsTrain= inputs.Get(0, traintPos);
            double[][] inputsTest = inputs.Get(traintPos, testPos);



            //simple linear regression model
            var ols = new OrdinaryLeastSquares()
            {
                UseIntercept = true
            };

            //linear regression model for several features
            MultipleLinearRegression regression = ols.Learn(inputsTrain, OutResPositiveTrain);

            //make a prediction
            double[] predicted = regression.Transform(inputsTest);

            //console output

            for (int i = 0; i < testPos - traintPos; i++)
            {
                Console.WriteLine("predicted: {0}   real: {1}", predicted[i], OutResPositiveTest[i]);
            }
            // And  print the squared error using the SquareLoss class:
            Console.WriteLine("error = {0}", new SquareLoss(OutResPositiveTest).Loss(predicted));

            // print the coefficient of determination
            double r2 = new RSquaredLoss(numberOfInputs: 29, expected: OutResPositiveTest).Loss(predicted); 
            Console.WriteLine("R^2 = {0}", r2);

            // alternative print the coefficient of determination
            double ur2 = regression.CoefficientOfDetermination(inputs, OutResPositiveTest, adjust: true);
            Console.WriteLine("alternative version of R2 = {0}", r2);

            Console.WriteLine("Press enter and close chart to exit");

            // for chart 

            int[] classes = new int[allData];
            double[] mountX = new double[allData];
            for (int i = 0; i < allData; i++)
            {
                if (i<testPos)
                {
                   // for csv data
                    mountX[i] = i+1;
                    classes[i] = 0; //csv data is class 0
                }
                else
                {
                    //for predicted
                    mountX[i] = i- (testPos - traintPos)+1;
                    classes[i] = 1; //predicted is class 1
                }

                
            }

            // make points of chart
            List<double> OutChart = new List<double>();
            OutChart.AddRange(OutResPositive);
            OutChart.AddRange(predicted);

           
            // plot chart
            ScatterplotBox.Show("res_positive from months", mountX, OutChart.ToArray(), classes).Hold();

            // for pause
            Console.ReadLine();
        }
    }
}

Imports System
Imports System.Linq
Imports Accord.Statistics.Models.Regression.Linear
Imports Accord.IO
Imports Accord.Math
Imports System.Data
Imports System.Collections.Generic
Imports Accord.Controls
Imports Accord.Math.Optimization.Losses

Module Program
    Sub Main()
        'for separating the training and test samples
        Dim traintPos As Integer = 18
        Dim testPos As Integer = 22
        Dim allData As Integer = testPos + (testPos - traintPos)

        'for correct reading symbol of float point in csv
        Dim customCulture As System.Globalization.CultureInfo = CType(System.Threading.Thread.CurrentThread.CurrentCulture.Clone(), System.Globalization.CultureInfo)
        customCulture.NumberFormat.NumberDecimalSeparator = "."
        System.Threading.Thread.CurrentThread.CurrentCulture = customCulture

        'read data
        Dim CsvFilePath As String = "msc_appel_data.csv"
        Dim mscTable As DataTable = New CsvReader(CsvFilePath, True).ToTable()

        'for encoding the string values of months into numerical values
        Dim monthNames As Dictionary(Of String, Double) = New Dictionary(Of String, Double) From
            {{"January", 1}, {"February", 2}, {"March", 3}, {"April", 4}, {"May", 5}, {"June", 6},
            {"July", 7}, {"August", 8}, {"September", 9},
            {"October", 10}, {"November", 11}, {"December", 12}}
        Dim months As String() = mscTable.Columns("month").ToArray(Of String)()
        Dim dMonths As Double() = New Double(months.Length - 1) {}
        For i As Integer = 0 To months.Length - 1
            dMonths(i) = monthNames(months(i))
        Next

        'select the target column
        Dim OutResPositive As Double() = mscTable.Columns("res_positive").ToArray()

        'separation of the test and train target sample
        Dim OutResPositiveTrain As Double() = OutResPositive.[Get](0, traintPos)
        Dim OutResPositiveTest As Double() = OutResPositive.[Get](traintPos, testPos)

        'deleting unneeded columns
        mscTable.Columns.Remove("total_appeals")
        mscTable.Columns.Remove("month")
        mscTable.Columns.Remove("res_positive")
        mscTable.Columns.Remove("year")

        'add coded in a double column month into Table
        'create new column

        Dim newCol As DataColumn = New DataColumn("dMonth", GetType(Double))
        newCol.AllowDBNull = True

        'add new column
        mscTable.Columns.Add(newCol)

        'fill new column
        Dim counter As Integer = 0
        For Each row As DataRow In mscTable.Rows
            row("dMonth") = dMonths(counter)
            counter += 1
        Next


        'receiving input data from a table
        Dim inputs As Double()() = mscTable.ToArray()

        'separation of the test and train sample
        Dim inputsTrain As Double()() = inputs.[Get](0, traintPos)
        Dim inputsTest As Double()() = inputs.[Get](traintPos, testPos)

        'simple linear regression model
        Dim ols = New OrdinaryLeastSquares() With {.UseIntercept = True}

        'linear regression model for several features
        Dim regression As MultipleLinearRegression = ols.Learn(inputsTrain, OutResPositiveTrain)

        'make a prediction
        Dim predicted As Double() = regression.Transform(inputsTest)

        'console output
        For i As Integer = 0 To testPos - traintPos - 1
            Console.WriteLine("predicted: {0}   real: {1}", predicted(i), OutResPositiveTest(i))
        Next

        'And  print the squared error using the SquareLoss class
        Console.WriteLine("error = {0}", New SquareLoss(OutResPositiveTest).Loss(predicted))

        'print the coefficient of determination
        Dim r2 As Double = New RSquaredLoss(numberOfInputs:=29, expected:=OutResPositiveTest).Loss(predicted)
        Console.WriteLine("R^2 = {0}", r2)

        'alternative print the coefficient of determination
        Dim ur2 As Double = regression.CoefficientOfDetermination(inputs, OutResPositiveTest, adjust:=True)
        Console.WriteLine("alternative version of R2 = {0}", r2)


        Console.WriteLine("Press enter and close chart to exit")

        'for chart 
        Dim classes As Integer() = New Integer(allData - 1) {}
        Dim mountX As Double() = New Double(allData - 1) {}
        For i As Integer = 0 To allData - 1
            If i < testPos Then
                mountX(i) = i + 1
                classes(i) = 0 'csv data is class 0
            Else
                mountX(i) = i - (testPos - traintPos) + 1
                classes(i) = 1 'predicted is class 1
            End If
        Next

        'make points of chart
        Dim OutChart As List(Of Double) = New List(Of Double)()
        OutChart.AddRange(OutResPositive)
        OutChart.AddRange(predicted)

        'plot chart
        ScatterplotBox.Show("res_positive from months", mountX, OutChart.ToArray(), classes).Hold()

        'for pause
        Console.ReadLine()
    End Sub

End Module