├── Chapter02 ├── DataOrganization.m ├── Exporting.m ├── Ferrari.txt ├── Importing.m ├── MyMatrix.csv ├── MyMatrix.txt ├── MyMatrix.xls ├── apollo.wav ├── capri.xlsx ├── coliseum.jpg ├── matrix.csv ├── matrix.txt └── museum.xls ├── Chapter03 ├── CleaningData.m ├── CleaningData.xlsx ├── EmergencyCalls.xlsx ├── EmergencyCallsNew.xlsx ├── ExploratoryStatistics.m ├── ExploratoryVisualization.m ├── GlassIdentificationDataSet.xlsx └── LifeExpectancy.xlsx ├── Chapter04 ├── AirfoilSelfNoise.csv ├── RegressionExample.m ├── VehiclesItaly.xlsx └── employees.xlsx ├── Chapter05 ├── DecisionTrees.m ├── DiscriminantAnalysis.m ├── NaiveBayes.m └── NearestNeighborCassifiers.m ├── Chapter06 ├── ClimaticData.xls ├── GaussianMixtureModels.m ├── HierarchicalClustering.m ├── KMeansExample.m ├── KMedoids Example.m ├── Minerals.xls └── PeripheralLocations.xls ├── Chapter07 └── EngineFitting.m ├── Chapter08 ├── PrincipalComponentAnalysis.m ├── StepwiseRegression.m ├── seeds_dataset.csv └── yacht_hydrodynamics.csv ├── Chapter09 ├── ClusterData.dat ├── ConcreteQualityFitting.m ├── Concrete_Data.xls ├── StudentClustering.m └── ThyroidDisease.m ├── LICENSE └── README.md /Chapter02/DataOrganization.m: -------------------------------------------------------------------------------- 1 | MyFamily = {'Luigi', 'Simone', 'Tiziana'; 13, 11, 43} 2 | MyFamily2= MyFamily(1:2,1:2) 3 | LastCell= MyFamily{2,3} 4 | class(LastCell) 5 | MyFamily2{2,2}=110 6 | MyFamily{1:2,1:3} 7 | [r1c1, r2c1, r1c2, r2c2, r1c3, r2c3]= MyFamily{1:2,1:3} 8 | class(r1c1) 9 | class(r2c1) 10 | Age = [MyFamily{2,:}] 11 | MyFamily=[MyFamily;{'M','M','F'}] 12 | %Structure Array 13 | field1 = 'Name'; 14 | value1 = {'Luigi','Simone','Tiziana'} 15 | field2 = 'Amount'; 16 | value2 = {150000,250000,50000}; 17 | field3 = 'Data'; 18 | value3 = {[25, 65, 43; 150, 168, 127.5; 280, 110, 170],[5, 5, 23; 120, 118, 107.5; 200, 100, 140],[15, 45, 23; 160, 158, 12; 230, 140, 160]}; 19 | customers = struct(field1,value1,field2,value2,field3,value3) 20 | customers(1).Name 21 | customers(1).Amount 22 | customers(1).Data 23 | class(customers) 24 | customers(4).Name='Giuseppe'; 25 | customers(4) 26 | bar(customers(1).Data) 27 | title(['Data of first customer: ', customers(1).Name]) 28 | customers(1:2).Data 29 | customers(1).Data(1:2,1:2) 30 | %Table 31 | load hospital 32 | whos 33 | LastName=hospital.LastName; 34 | Sex=hospital.Sex; 35 | Age=hospital.Age; 36 | Weight=hospital.Weight; 37 | TablePatients = table(LastName,Sex,Age,Weight); 38 | TablePatients(1:5,:) 39 | BlPrMax=hospital.BloodPressure(:,1); 40 | BlPrMin=hospital.BloodPressure(:,2); 41 | TablePatients.BlPrMax=BlPrMax; 42 | TablePatients.BlPrMin=BlPrMin; 43 | TablePatients(1:5,[1 5:6]) 44 | summary(TablePatients) 45 | %Categorical Array 46 | SexC=categorical(Sex); 47 | categories(SexC) 48 | AgeC=categorical(Age); 49 | categories(AgeC) 50 | NameBin = {'FirstBin', 'SecondBin', 'ThirdBin'}; 51 | AgeBin = discretize(Age,[25 33 41 50],'categorical',NameBin); 52 | summary(AgeBin) 53 | 54 | 55 | -------------------------------------------------------------------------------- /Chapter02/Exporting.m: -------------------------------------------------------------------------------- 1 | MyMatrix = rand(5) 2 | dlmwrite('MyMatrix.txt', MyMatrix) 3 | type('MyMatrix.txt') 4 | MyMatrix = rand(5) 5 | xlswrite('MyMatrix.xls', MyMatrix) 6 | MyMatrix = rand(5) 7 | csvwrite('MyMatrix.csv', MyMatrix) 8 | Coliseum = imread('coliseum.jpg'); 9 | imwrite(Coliseum,'coliseum2.jpg'); 10 | [apollo13,Fs] = audioread('apollo13.wav'); 11 | sound(apollo13,Fs) 12 | Apollo13Obj = audioplayer(apollo13,Fs); 13 | play(Apollo13Obj); 14 | audiowrite('apollo13.wav',apollo13,Fs) 15 | InfoAudio = audioinfo('apollo13.wav') -------------------------------------------------------------------------------- /Chapter02/Ferrari.txt: -------------------------------------------------------------------------------- 1 | 2014 Ferrari458SpecialeA 4497 605 2 | 2015 Ferrari488GTB 3902 670 3 | 2015 Ferrari488Spider 3902 670 4 | 2016 FerrariJ50 3902 690 5 | 2017 Ferrari812Superfast 6496 800 6 | -------------------------------------------------------------------------------- /Chapter02/Importing.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter02/Importing.m -------------------------------------------------------------------------------- /Chapter02/MyMatrix.csv: -------------------------------------------------------------------------------- 1 | 0.34998,0.35166,0.28584,0.075854,0.12991 2 | 0.1966,0.83083,0.7572,0.05395,0.56882 3 | 0.25108,0.58526,0.75373,0.5308,0.46939 4 | 0.61604,0.54972,0.38045,0.77917,0.011902 5 | 0.47329,0.91719,0.56782,0.93401,0.33712 6 | -------------------------------------------------------------------------------- /Chapter02/MyMatrix.txt: -------------------------------------------------------------------------------- 1 | 0.75774,0.70605,0.82346,0.43874,0.48976 2 | 0.74313,0.031833,0.69483,0.38156,0.44559 3 | 0.39223,0.27692,0.3171,0.76552,0.64631 4 | 0.65548,0.046171,0.95022,0.7952,0.70936 5 | 0.17119,0.097132,0.034446,0.18687,0.75469 6 | -------------------------------------------------------------------------------- /Chapter02/MyMatrix.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter02/MyMatrix.xls -------------------------------------------------------------------------------- /Chapter02/apollo.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter02/apollo.wav -------------------------------------------------------------------------------- /Chapter02/capri.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter02/capri.xlsx -------------------------------------------------------------------------------- /Chapter02/coliseum.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter02/coliseum.jpg -------------------------------------------------------------------------------- /Chapter02/matrix.csv: -------------------------------------------------------------------------------- 1 | 10,20,30 2 | 40,50,60 3 | 70,80,90 -------------------------------------------------------------------------------- /Chapter02/matrix.txt: -------------------------------------------------------------------------------- 1 | 10,20,30 2 | 40,50,60 3 | 70,80,90 -------------------------------------------------------------------------------- /Chapter02/museum.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter02/museum.xls -------------------------------------------------------------------------------- /Chapter03/CleaningData.m: -------------------------------------------------------------------------------- 1 | SampleData = readtable('CleaningData.xlsx'); 2 | summary(SampleData) 3 | SampleData 4 | %Find missing values 5 | id = {'NA' '' '-19' -19 NaN '.'}; 6 | WrongPos = ismissing(SampleData,id); 7 | SampleData(any(WrongPos,2),:) 8 | %Change data type 9 | SampleData.age = str2double(SampleData.age); 10 | summary(SampleData(:,3)) 11 | SampleData.right = str2double(SampleData.right); 12 | summary(SampleData(:,4)) 13 | %Replace missing value 14 | SampleData = standardizeMissing(SampleData,-19); 15 | summary(SampleData(:,3)) 16 | SampleDataNew = fillmissing(SampleData,'previous') 17 | %Removing missing entries 18 | SampleDataMinor = rmmissing(SampleData) 19 | %Ordering the table 20 | SampleDataOrdered = sortrows(SampleDataMinor,{'age'},{'descend'}) 21 | %Find outliers in data 22 | SampleDataOutlier = isoutlier(SampleDataNew(2:end,3:5)) 23 | %Organizing 24 | SampleData1 = SampleDataNew(1:6,:) 25 | SampleData2 = SampleDataNew(7:12,:) 26 | SampleDataComplete = [SampleData1;SampleData2] 27 | LifeExpectancy = readtable('LifeExpectancy.xlsx') 28 | SampleDataLE = join(SampleDataComplete,LifeExpectancy,'Keys','gender') 29 | -------------------------------------------------------------------------------- /Chapter03/CleaningData.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter03/CleaningData.xlsx -------------------------------------------------------------------------------- /Chapter03/EmergencyCalls.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter03/EmergencyCalls.xlsx -------------------------------------------------------------------------------- /Chapter03/EmergencyCallsNew.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter03/EmergencyCallsNew.xlsx -------------------------------------------------------------------------------- /Chapter03/ExploratoryStatistics.m: -------------------------------------------------------------------------------- 1 | GlassIdentificationDataSet = readtable('GlassIdentificationDataSet.xlsx'); 2 | Max = max(GlassIdentificationDataSet{:,3:8}) 3 | Mean = mean(GlassIdentificationDataSet{:,3:8}) 4 | Min = min(GlassIdentificationDataSet{:,3:8}) 5 | [Max,IndRowMax] = max(GlassIdentificationDataSet{:,3:8}) 6 | [Min,IndRowMin] = min(GlassIdentificationDataSet{:,3:8}) 7 | Median = median(GlassIdentificationDataSet{:,3:8}) 8 | Mode = mode(GlassIdentificationDataSet{:,3:8}) 9 | Quantile = quantile(GlassIdentificationDataSet{:,3:8}, [0.25 0.50 0.75]) 10 | Percentiles = prctile(GlassIdentificationDataSet{:,3:8}, [25 50 75]) 11 | Range = range(GlassIdentificationDataSet{:,3:8}) 12 | Iqr = iqr(GlassIdentificationDataSet{:,3:8}) 13 | CheckIqr = Quantile(3,:) - Quantile(1,:) 14 | Variance = var(GlassIdentificationDataSet{:,3:8}) 15 | StDev = std(GlassIdentificationDataSet{:,3:8}) 16 | Mad = mad(GlassIdentificationDataSet{:,3:8}) 17 | MatA =rand(4) 18 | Cov = cov(MatA) 19 | MatB = rand(3) 20 | Cor = corrcoef(MatB) 21 | MatC=rand(3) 22 | Col4=5+MatC(:,3)*3 23 | MatC=[MatC Col4] 24 | CorC = corrcoef(MatC) 25 | a = [-5:.1:5]; 26 | Norm = normpdf(a,0,1); 27 | figure; 28 | plot(a,Norm) 29 | SkN = skewness(GlassIdentificationDataSet{:,3:8}) 30 | Kurt = kurtosis(GlassIdentificationDataSet{:,3:8}) -------------------------------------------------------------------------------- /Chapter03/ExploratoryVisualization.m: -------------------------------------------------------------------------------- 1 | EmergencyCalls = xlsread('EmergencyCalls.xlsx'); 2 | [rows,cols] = size(EmergencyCalls); 3 | x = 1:rows; 4 | plot(x,EmergencyCalls) 5 | legend('Street 1','Street 2','Street 3','Street 4') 6 | xlabel('Time') 7 | ylabel('Emergency Calls') 8 | ystats 9 | PlotStat (x,EmergencyCallsNew) 10 | histogram(x) 11 | Vect1=[10,25,12,13,33,25,44,50,43,26,38,32,31,28,30]; 12 | histogram(Vect1) 13 | Vect2=[10,25,12,13,33,25,44,50,43,26,38,32,31,28,30,15,16,22,35,18]; 14 | h = histogram(Vect2,12) 15 | xlabel('Results') 16 | ylabel('Frequency') 17 | title('Customer Satisfaction Survey') 18 | h.FaceColor = [0 0.5 0.5]; 19 | h = histogram(Vect2,12) 20 | xlabel('Results') 21 | ylabel('Frequency') 22 | title('Customer Satisfaction Survey') 23 | h.FaceColor = [0 0.5 0.5]; 24 | Vect3=[10,25,12,13,33,25,44,50,43,26,38,32,31,28,30,15,16,22,35,18]; 25 | nbin=10:10:50; 26 | h = histogram(Vect3,nbin) 27 | xlabel('Results') 28 | ylabel('Frequency') 29 | title('Customer Satisfaction Survey') 30 | h.FaceColor = [0 0.5 0.5]; 31 | Vect4 = randn(1000,1); 32 | h = histogram(Vect4,'Normalization','pdf') 33 | Vect5 = normrnd(50,3,1000,1); 34 | Hist = histfit(Vect5) 35 | normrnd(mu,sigma) 36 | r=normrnd(3,1,100,1); 37 | data1=normrnd(3,2,100,1); 38 | data2=normrnd(2,1,100,1); 39 | data3=normrnd(6,2,100,1); 40 | data4=normrnd(8,0.5,100,1); 41 | data5=normrnd(4,4,100,1); 42 | data6=normrnd(5,1,100,1); 43 | data=[data1 data2 data3 data4 data5 data6]; 44 | boxplot(data) 45 | boxplot(data,'Labels',{'mu = 3','mu = 2','mu = 6','mu = 8','mu = 4','mu = 5'}) 46 | boxplot(dati,'Labels',{'mu = 3','mu = 2','mu = 6','mu = 8','mu = 4','mu = 5'}, 'LabelOrientation', 'inline') 47 | vect1=normrnd(4,2,100,1); 48 | vect2=normrnd(7,0.5,100,1); 49 | figure 50 | boxplot([vect1,vect2],'Notch','on','Labels',{'mu = 4','mu = 7'}) 51 | title('Comparison between two distributions') 52 | Height = [168 168 168 173 163 174 174 174 175 175 176 165 180 180 182 182 183 186 191 191 192 165 167 174 176 167]; 53 | Weight = [65 65 65 78 70 68 68 80 70 75 77 69 80 65 79 79 79 80 81 81 82 69 69 77 68 70]; 54 | scatter(Weight,Height) 55 | IdealWeight=Height-100-[(Height-150)/4]; 56 | hold on 57 | plot(IdealWeight,Height) 58 | RandomMatrix = randn(100,4); 59 | plotmatrix(RandomMatrix) 60 | 61 | 62 | -------------------------------------------------------------------------------- /Chapter03/GlassIdentificationDataSet.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter03/GlassIdentificationDataSet.xlsx -------------------------------------------------------------------------------- /Chapter03/LifeExpectancy.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter03/LifeExpectancy.xlsx -------------------------------------------------------------------------------- /Chapter04/RegressionExample.m: -------------------------------------------------------------------------------- 1 | %Least-squares regression 2 | VehicleData = readtable('VehiclesItaly.xlsx'); 3 | summary(VehicleData) 4 | y=VehicleData.Registrations; 5 | x=VehicleData.Population; 6 | alpha=x\y 7 | VehicleRegFit=alpha*x; 8 | scatter(x,y) 9 | hold on 10 | plot(x,VehicleRegFit) 11 | Residual=y-VehicleRegFit; 12 | stem(Residual) 13 | Rsq1 = 1 - sum((y - VehicleRegFit).^2)/sum((y - mean(y)).^2) 14 | X = [ones(length(x),1) x]; 15 | alpha_beta = X\y 16 | VehicleRegFit2 = X* alpha_beta; 17 | scatter(x,y) 18 | hold on 19 | plot(x,VehicleRegFit) 20 | plot(x,VehicleRegFit2,'--b') 21 | Rsq2 = 1 - sum((y - VehicleRegFit2).^2)/sum((y - mean(y)).^2) 22 | 23 | %Basic Fitting interface 24 | VehicleData = readtable('VehiclesItaly.xlsx'); 25 | scatter(VehicleData.Population,VehicleData.Registrations) 26 | 27 | %linear regression model 28 | VehicleData = readtable('VehiclesItaly.xlsx'); 29 | VehicleData(1:10,:) 30 | lrm = fitlm(VehicleData,'Registrations~Population') 31 | plotResiduals(lrm) 32 | plotResiduals(lrm,'probability') 33 | outliers = find(lrm.Residuals.Raw < -1.5*10^5) 34 | lrm2 = fitlm(VehicleData,'Registrations~Population','Exclude',outliers) 35 | plotResiduals(lrm2,'probability') 36 | lrm1 = fitlm(VehicleData,'Registrations~Population') 37 | lrm2 = fitlm(VehicleData,'Registrations~Population','RobustOpts','on') 38 | subplot(1,2,1) 39 | plotResiduals(lrm1,'probability') 40 | subplot(1,2,2) 41 | plotResiduals(lrm2,'probability') 42 | outliers = find((lrm2.Residuals.Raw < -1.5*10^5) | (lrm2.Residuals.Raw > 1.5*10^5)) 43 | lrm3 = fitlm(VehicleData,'Registrations~Population','RobustOpts','on','Exclude',outliers) 44 | subplot(1,2,1) 45 | plotResiduals(lrm2,'probability') 46 | subplot(1,2,2) 47 | plotResiduals(lrm3,'probability') 48 | 49 | %Multiple Linear Regression 50 | load hald 51 | whos 52 | X = [ones(13,1), ingredients]; 53 | Y=(heat); 54 | A=X\Y 55 | lm=fitlm(ingredients,Y) 56 | b=regress(Y,X) 57 | 58 | %MLR with Categorical Predictor 59 | EmployeesSalary = readtable('employees.xlsx'); 60 | summary(EmployeesSalary) 61 | EmployeesSalary.LevelOfEmployee=categorical(EmployeesSalary.LevelOfEmployee); 62 | class(EmployeesSalary.LevelOfEmployee) 63 | figure() 64 | gscatter(EmployeesSalary.YearsExperience, EmployeesSalary. Salary, EmployeesSalary.LevelOfEmployee,'bgr','x.o') 65 | title('Salary of Employees versus Years of the Experience, Grouped by Level of Employee') 66 | LMcat = fitlm(EmployeesSalary,'Salary~YearsExperience*LevelOfEmployee') 67 | Xvalues = linspace(min(EmployeesSalary.YearsExperience),max(EmployeesSalary.YearsExperience)); 68 | figure() 69 | gscatter(EmployeesSalary.YearsExperience, EmployeesSalary.Salary, EmployeesSalary.LevelOfEmployee,'bgr','x.o') 70 | title('Salary of Employees versus Years of the Experience, Grouped by Level of Employee') 71 | line(Xvalues,feval(LMcat,Xvalues,'GeneralStaff'),'Color','b','LineWidth',2) 72 | line(Xvalues,feval(LMcat,Xvalues,'TechnicalStaff'),'Color','r','LineWidth',2) 73 | line(Xvalues,feval(LMcat,Xvalues,'Management'),'Color','g','LineWidth',2) 74 | 75 | %Polynomial regression 76 | Time = [6 8 11 14 16 18 19]; 77 | Temp = [4 7 10 12 11.5 9 7]; 78 | plot(Time,Temp,'o') 79 | title('Plot of Temperature Versus Time') 80 | coeff = polyfit(Time,Temp,2) 81 | TimeNew = 6:0.1:19; 82 | TempNew = polyval(coeff,TimeNew); 83 | figure 84 | plot(Time,Temp,'o', TimeNew, TempNew) 85 | title('Plot of Data (Points) and Model (Line)') 86 | 87 | %Regression Learner App 88 | url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00291/airfoil_self_noise.dat'; 89 | websave('AirfoilSelfNoise.csv',url); 90 | varnames = {' FreqH '; 'AngleD'; 'ChLenM'; 'FStVelMs'; ' SucSDTM ';' SPLdB'}; 91 | AirfoilSelfNoise = readtable('AirfoilSelfNoise.csv'); 92 | AirfoilSelfNoise.Properties.VariableNames = varnames; 93 | 94 | 95 | 96 | 97 | 98 | -------------------------------------------------------------------------------- /Chapter04/VehiclesItaly.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter04/VehiclesItaly.xlsx -------------------------------------------------------------------------------- /Chapter04/employees.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter04/employees.xlsx -------------------------------------------------------------------------------- /Chapter05/DecisionTrees.m: -------------------------------------------------------------------------------- 1 | X=rand(100,2); 2 | Y=(X(:,1)<0.3 | X(:,2)>0.6); 3 | SimpleTree=fitctree(X,Y) 4 | view(SimpleTree) 5 | view(SimpleTree,'mode','graph') 6 | 7 | %FisherIris dataset 8 | load fisheriris 9 | tabulate(species) 10 | gplotmatrix(a,b,group) 11 | gplotmatrix(meas, meas, species); 12 | gscatter(meas(:,3), meas(:,4), species,'rgb','osd'); 13 | xlabel('Petal length'); 14 | ylabel('Petal width'); 15 | ClassTree= fitctree(meas,species); 16 | view(ClassTree) 17 | view(ClassTree,'mode','graph') 18 | MeasNew= [5.9 3.2 1.3 0.25]; 19 | predict(ClassTree,measNew) 20 | resuberror = resubLoss(ClassTree) 21 | cvrtree = crossval(ClassTree) 22 | cvloss = kfoldLoss(cvrtree) 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | -------------------------------------------------------------------------------- /Chapter05/DiscriminantAnalysis.m: -------------------------------------------------------------------------------- 1 | load fisheriris 2 | DiscrModel = fitcdiscr(meas,species) 3 | DiscrModel.Mu 4 | DiscrModel.Coeffs 5 | X = [meas(:,3) meas(:,4)]; 6 | DiscrModelPetal = fitcdiscr(X,species) 7 | gscatter(meas(:,3), meas(:,4), species,'rgb','osd'); 8 | Const12 = DiscrModelPetal.Coeffs(1,2).Const; 9 | Linear12 = DiscrModelPetal.Coeffs(1,2).Linear; 10 | hold on 11 | Bound12 = @(x1,x2) Const12 + Linear12(1)*x1 + Linear12(2)*x2; 12 | B12 = ezplot(Bound12,[0 7.2 0 2.8]); 13 | B12.Color = 'r'; 14 | B12.LineWidth = 2; 15 | Const23 = DiscrModelPetal.Coeffs(2,3).Const; 16 | Linear23 = DiscrModelPetal.Coeffs(2,3).Linear; 17 | Bound23 = @(x1,x2) Const23 + Linear23 (1)*x1 + Linear23 (2)*x2; 18 | B23 = ezplot(Bound23,[0 7.2 0 2.8]); 19 | B23.Color = 'b'; 20 | B23.LineWidth = 2; 21 | xlabel('Petal Length') 22 | ylabel('Petal Width') 23 | title('{\bf Linear Classification by Discriminant Analysis}') 24 | NewPointsX=[2 5 6]; 25 | NewPointsY=[0.5 1.5 2]; 26 | LabelsNewPoints = predict(DiscrModelPetal,[NewPointsX' NewPointsY']) 27 | plot(NewPointsX,NewPointsY,'*') 28 | DiscrModelResubErr = resubLoss(DiscrModel) 29 | PredictedValue = predict(DiscrModel,meas); 30 | ConfMat = confusionmat(species,PredictedValue) 31 | Err = ~strcmp(PredictedValue,species); 32 | gscatter(meas(:,3), meas(:,4), species,'rgb','osd'); 33 | hold on 34 | plot(meas(Err,3), meas(Err,4), 'kx'); 35 | xlabel('Petal length'); 36 | ylabel('Petal width'); 37 | 38 | 39 | 40 | 41 | 42 | -------------------------------------------------------------------------------- /Chapter05/NaiveBayes.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter05/NaiveBayes.m -------------------------------------------------------------------------------- /Chapter05/NearestNeighborCassifiers.m: -------------------------------------------------------------------------------- 1 | load fisheriris 2 | KnnModel = fitcknn(meas,species,'NumNeighbors',3) 3 | KnnModel.ClassNames 4 | Knn3ResubErr = resubLoss(KnnModel) 5 | PredictedValue = predict(KnnModel,meas); 6 | ConfMat = confusionmat(species,PredictedValue) 7 | CVModel = crossval(KnnModel) 8 | KLossModel = kfoldLoss(CVModel) 9 | KnnModel.NumNeighbors = 5 10 | Knn5ResubErr = resubLoss(KnnModel) 11 | CVModel = crossval(KnnModel); 12 | K5LossModel = kfoldLoss(CVModel) 13 | PredictedValue = predict(KnnModel,meas); 14 | ConfMat = confusionmat(species,PredictedValue) 15 | KnnModel2 = fitcknn(meas,species,'NSMethod','exhaustive','Distance','cosine','NumNeighbors',5); 16 | Knn5ResubErr2 = resubLoss(KnnModel2) 17 | PredictedValue = predict(KnnModel2,meas); 18 | ConfMat = confusionmat(species,PredictedValue) 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | -------------------------------------------------------------------------------- /Chapter06/ClimaticData.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter06/ClimaticData.xls -------------------------------------------------------------------------------- /Chapter06/GaussianMixtureModels.m: -------------------------------------------------------------------------------- 1 | Data = xlsread('ClimaticData.xls'); 2 | gscatter(Data(:,1),Data(:,2)) 3 | GMModel = fitgmdist(Data,2) 4 | gscatter(Data(:,1),Data(:,2)) 5 | hold on 6 | ezcontour(@(x1,x2)pdf(GMModel,[x1 x2]),[0 45 0 30]) 7 | IdCluster = cluster(GMModel,Data); 8 | gscatter(Data(:,1), Data(:,2), IdCluster,'bg','xo') 9 | CenterCluster = GMModel.mu; 10 | hold on 11 | plot(CenterCluster(:,1),CenterCluster(:,2),'x','LineWidth',4,'MarkerEdgeColor','k','MarkerSize',25) 12 | ezcontour(@(x1,x2)pdf(GMModel,[x1 x2]),[0 45 0 30]) 13 | mu = GMModel.mu; 14 | sigma = GMModel.Sigma; 15 | DistObj = gmdistribution(mu,sigma); 16 | ezsurf(@(x,y)pdf(DistObj,[x y]),[0 45],[0 30]) 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | -------------------------------------------------------------------------------- /Chapter06/HierarchicalClustering.m: -------------------------------------------------------------------------------- 1 | DataPoints = [100 100;90 90;10 10;10 20;90 70;50 50] 2 | DistanceCalc = pdist(DataPoints) 3 | DistanceMatrix = squareform(DistanceCalc) 4 | GroupsMatrix = linkage(DistanceCalc) 5 | dendrogram(GroupsMatrix) 6 | VerifyDistaces = cophenet(GroupsMatrix, DistanceCalc) 7 | NewDistanceCalc = pdist(DataPoints, 'cosine') 8 | NewGroupsMatrix = linkage(NewDistanceCalc,'weighted') 9 | NewVerifyDistaces = cophenet(NewGroupsMatrix, NewDistanceCalc) 10 | -------------------------------------------------------------------------------- /Chapter06/KMeansExample.m: -------------------------------------------------------------------------------- 1 | Minerals = xlsread('Minerals.xls'); 2 | InputData = Minerals(:,1:2); 3 | gscatter(InputData(:,1), InputData(:,2)) 4 | rng(1); 5 | [IdCluster,Centroid] = kmeans(InputData,4); 6 | gscatter(InputData(:,1), InputData(:,2), IdCluster,'bgrm','x*o^') 7 | hold on 8 | plot(Centroid(:,1),Centroid(:,2),'x','LineWidth',4,'MarkerEdgeColor','k','MarkerSize',25) 9 | [IdCluster3,Centroid3] = kmeans(InputData,3); 10 | silhouette(InputData, IdCluster3) 11 | EvaluateK = evalclusters(InputData,'kmeans','CalinskiHarabasz','KList',[1:6]) 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | -------------------------------------------------------------------------------- /Chapter06/KMedoids Example.m: -------------------------------------------------------------------------------- 1 | PerLoc = xlsread('PeripheralLocations.xls'); 2 | gscatter(InputData (:,1), InputData (:,2)) 3 | [IdCluster,Kmedoid,SumDist,Dist,IdClKm,info] = kmedoids(PerLoc,3); 4 | info 5 | gscatter(PerLoc(:,1), PerLoc(:,2), IdCluster,'bgr','xo^') 6 | hold on 7 | plot(Kmedoid(:,1),Kmedoid(:,2),'x','LineWidth',4,'MarkerEdgeColor','k','MarkerSize',25) 8 | silhouette(PerLoc, IdCluster) 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | -------------------------------------------------------------------------------- /Chapter06/Minerals.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter06/Minerals.xls -------------------------------------------------------------------------------- /Chapter06/PeripheralLocations.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter06/PeripheralLocations.xls -------------------------------------------------------------------------------- /Chapter07/EngineFitting.m: -------------------------------------------------------------------------------- 1 | load engine_dataset 2 | inputs = engineInputs; 3 | targets = engineTargets; 4 | hiddenLayerSize = 10; 5 | net = fitnet(hiddenLayerSize); 6 | net.divideParam.trainRatio = 70/100; 7 | net.divideParam.valRatio = 15/100; 8 | net.divideParam.testRatio = 15/100; 9 | [net,tr] = train(net,inputs,targets); 10 | outputs = net(inputs); 11 | errors = gsubtract(outputs,targets); 12 | performance = perform(net,targets,outputs) 13 | view(net) 14 | % figure, plotperform(tr) 15 | % figure, plottrainstate(tr) 16 | % figure, plotfit(targets,outputs) 17 | % figure, plotregression(targets,outputs) 18 | % figure, ploterrhist(errors) 19 | -------------------------------------------------------------------------------- /Chapter08/PrincipalComponentAnalysis.m: -------------------------------------------------------------------------------- 1 | url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt'; 2 | websave('seeds_dataset.csv',url); 3 | varnames = {'Area'; 'Perimeter'; 'Compactness'; 'LengthK'; 'WidthK';'AsymCoef';'LengthKG';'Seeds'}; 4 | Seeds_dataset = readtable('seeds_dataset.csv'); 5 | Seeds_dataset.Properties.VariableNames = varnames; 6 | MissingValue = ismissing(Seeds_dataset); 7 | RowsMissValue = find(any(MissValue==1,2)); 8 | Seeds_dataset = rmmissing(Seeds_dataset); 9 | VarMeas = table2array((Seeds_dataset(:,1:7))); 10 | SeedClass = table2array((Seeds_dataset(:,8))); 11 | plotmatrix(VarMeas) 12 | r = corr(VarMeas) 13 | [coeff,score,latent,tsquared,explained,mu] = pca(VarMeas); 14 | coeff 15 | size(VarMeas) 16 | size(score) 17 | gscatter(score(:,1),score(:,2),SeedClass,'brg','xo^') 18 | latent 19 | plot(latent) 20 | xlabel('Principal Component') 21 | ylabel('Variance Explained ') 22 | biplot(coeff(:,1:2),'scores',score(:,1:2),'varlabels',varnames(1:7)); 23 | 24 | 25 | 26 | 27 | -------------------------------------------------------------------------------- /Chapter08/StepwiseRegression.m: -------------------------------------------------------------------------------- 1 | url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00243/yacht_hydrodynamics.data'; 2 | websave('yacht_hydrodynamics.csv',url); 3 | varnames = {'LongPos'; 'PrismaticCoef'; 'LengDispRatio'; 'BeamDraughtRatio'; ' LengthBeamRatio ';'FroudeNumber';'ResResistance'}; 4 | YachtHydrodynamics = readtable('yacht_hydrodynamics.csv'); 5 | YachtHydrodynamics.Properties.VariableNames = varnames; 6 | summary(YachtHydrodynamics) 7 | YachtHydrodynamicsClean = rmmissing(YachtHydrodynamics); 8 | size(YachtHydrodynamics) 9 | size(YachtHydrodynamicsClean) 10 | summary(YachtHydrodynamicsClean); 11 | X =table2array(YachtHydrodynamicsClean(:,1:6)); 12 | Y =table2array(YachtHydrodynamicsClean(:,7)); 13 | subplot(2,3,1) 14 | scatter(X(:,1),Y) 15 | subplot(2,3,2) 16 | scatter(X(:,2),Y) 17 | subplot(2,3,3) 18 | scatter(X(:,3),Y) 19 | subplot(2,3,4) 20 | scatter(X(:,4),Y) 21 | subplot(2,3,5) 22 | scatter(X(:,5),Y) 23 | subplot(2,3,6) 24 | scatter(X(:,6),Y) 25 | Model1 = stepwiselm(X,Y,'constant','ResponseVar','ResResistance') 26 | Model2 = stepwiselm(X,Y,'linear','ResponseVar','ResResistance') 27 | Model3 = stepwiselm(X,Y,'interactions','ResponseVar','ResResistance') 28 | Model4 = stepwiselm(X,Y,'quadratic','ResponseVar','ResResistance','Upper','quadratic') 29 | RSquared = [Model1.Rsquared.Adjusted,Model2.Rsquared.Adjusted,Model3.Rsquared.Adjusted,Model4.Rsquared.Adjusted] 30 | subplot(2,2,1) 31 | plotResiduals(Model1,'fitted') 32 | subplot(2,2,2) 33 | plotResiduals(Model2,'fitted') 34 | subplot(2,2,3) 35 | plotResiduals(Model3,'fitted') 36 | subplot(2,2,4) 37 | plotResiduals(Model4,'fitted') 38 | Rrange1 = [min(Model1.Residuals.Raw),max(Model1.Residuals.Raw)]; 39 | Rrange2 = [min(Model2.Residuals.Raw),max(Model2.Residuals.Raw)]; 40 | Rrange3 = [min(Model3.Residuals.Raw),max(Model3.Residuals.Raw)]; 41 | Rrange4 = [min(Model4.Residuals.Raw),max(Model4.Residuals.Raw)]; 42 | Rranges = [Rrange1;Rrange2;Rrange3;Rrange4] 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /Chapter08/seeds_dataset.csv: -------------------------------------------------------------------------------- 1 | 15.26 14.84 0.871 5.763 3.312 2.221 5.22 1 2 | 14.88 14.57 0.8811 5.554 3.333 1.018 4.956 1 3 | 14.29 14.09 0.905 5.291 3.337 2.699 4.825 1 4 | 13.84 13.94 0.8955 5.324 3.379 2.259 4.805 1 5 | 16.14 14.99 0.9034 5.658 3.562 1.355 5.175 1 6 | 14.38 14.21 0.8951 5.386 3.312 2.462 4.956 1 7 | 14.69 14.49 0.8799 5.563 3.259 3.586 5.219 1 8 | 14.11 14.1 0.8911 5.42 3.302 2.7 5 1 9 | 16.63 15.46 0.8747 6.053 3.465 2.04 5.877 1 10 | 16.44 15.25 0.888 5.884 3.505 1.969 5.533 1 11 | 15.26 14.85 0.8696 5.714 3.242 4.543 5.314 1 12 | 14.03 14.16 0.8796 5.438 3.201 1.717 5.001 1 13 | 13.89 14.02 0.888 5.439 3.199 3.986 4.738 1 14 | 13.78 14.06 0.8759 5.479 3.156 3.136 4.872 1 15 | 13.74 14.05 0.8744 5.482 3.114 2.932 4.825 1 16 | 14.59 14.28 0.8993 5.351 3.333 4.185 4.781 1 17 | 13.99 13.83 0.9183 5.119 3.383 5.234 4.781 1 18 | 15.69 14.75 0.9058 5.527 3.514 1.599 5.046 1 19 | 14.7 14.21 0.9153 5.205 3.466 1.767 4.649 1 20 | 12.72 13.57 0.8686 5.226 3.049 4.102 4.914 1 21 | 14.16 14.4 0.8584 5.658 3.129 3.072 5.176 1 22 | 14.11 14.26 0.8722 5.52 3.168 2.688 5.219 1 23 | 15.88 14.9 0.8988 5.618 3.507 0.7651 5.091 1 24 | 12.08 13.23 0.8664 5.099 2.936 1.415 4.961 1 25 | 15.01 14.76 0.8657 5.789 3.245 1.791 5.001 1 26 | 16.19 15.16 0.8849 5.833 3.421 0.903 5.307 1 27 | 13.02 13.76 0.8641 5.395 3.026 3.373 4.825 1 28 | 12.74 13.67 0.8564 5.395 2.956 2.504 4.869 1 29 | 14.11 14.18 0.882 5.541 3.221 2.754 5.038 1 30 | 13.45 14.02 0.8604 5.516 3.065 3.531 5.097 1 31 | 13.16 13.82 0.8662 5.454 2.975 0.8551 5.056 1 32 | 15.49 14.94 0.8724 5.757 3.371 3.412 5.228 1 33 | 14.09 14.41 0.8529 5.717 3.186 3.92 5.299 1 34 | 13.94 14.17 0.8728 5.585 3.15 2.124 5.012 1 35 | 15.05 14.68 0.8779 5.712 3.328 2.129 5.36 1 36 | 16.12 15 0.9 5.709 3.485 2.27 5.443 1 37 | 16.2 15.27 0.8734 5.826 3.464 2.823 5.527 1 38 | 17.08 15.38 0.9079 5.832 3.683 2.956 5.484 1 39 | 14.8 14.52 0.8823 5.656 3.288 3.112 5.309 1 40 | 14.28 14.17 0.8944 5.397 3.298 6.685 5.001 1 41 | 13.54 13.85 0.8871 5.348 3.156 2.587 5.178 1 42 | 13.5 13.85 0.8852 5.351 3.158 2.249 5.176 1 43 | 13.16 13.55 0.9009 5.138 3.201 2.461 4.783 1 44 | 15.5 14.86 0.882 5.877 3.396 4.711 5.528 1 45 | 15.11 14.54 0.8986 5.579 3.462 3.128 5.18 1 46 | 13.8 14.04 0.8794 5.376 3.155 1.56 4.961 1 47 | 15.36 14.76 0.8861 5.701 3.393 1.367 5.132 1 48 | 14.99 14.56 0.8883 5.57 3.377 2.958 5.175 1 49 | 14.79 14.52 0.8819 5.545 3.291 2.704 5.111 1 50 | 14.86 14.67 0.8676 5.678 3.258 2.129 5.351 1 51 | 14.43 14.4 0.8751 5.585 3.272 3.975 5.144 1 52 | 15.78 14.91 0.8923 5.674 3.434 5.593 5.136 1 53 | 14.49 14.61 0.8538 5.715 3.113 4.116 5.396 1 54 | 14.33 14.28 0.8831 5.504 3.199 3.328 5.224 1 55 | 14.52 14.6 0.8557 5.741 3.113 1.481 5.487 1 56 | 15.03 14.77 0.8658 5.702 3.212 1.933 5.439 1 57 | 14.46 14.35 0.8818 5.388 3.377 2.802 5.044 1 58 | 14.92 14.43 0.9006 5.384 3.412 1.142 5.088 1 59 | 15.38 14.77 0.8857 5.662 3.419 1.999 5.222 1 60 | 12.11 13.47 0.8392 5.159 3.032 1.502 4.519 1 61 | 11.42 12.86 0.8683 5.008 2.85 2.7 4.607 1 62 | 11.23 12.63 0.884 4.902 2.879 2.269 4.703 1 63 | 12.36 13.19 0.8923 5.076 3.042 3.22 4.605 1 64 | 13.22 13.84 0.868 5.395 3.07 4.157 5.088 1 65 | 12.78 13.57 0.8716 5.262 3.026 1.176 4.782 1 66 | 12.88 13.5 0.8879 5.139 3.119 2.352 4.607 1 67 | 14.34 14.37 0.8726 5.63 3.19 1.313 5.15 1 68 | 14.01 14.29 0.8625 5.609 3.158 2.217 5.132 1 69 | 14.37 14.39 0.8726 5.569 3.153 1.464 5.3 1 70 | 12.73 13.75 0.8458 5.412 2.882 3.533 5.067 1 71 | 17.63 15.98 0.8673 6.191 3.561 4.076 6.06 2 72 | 16.84 15.67 0.8623 5.998 3.484 4.675 5.877 2 73 | 17.26 15.73 0.8763 5.978 3.594 4.539 5.791 2 74 | 19.11 16.26 0.9081 6.154 3.93 2.936 6.079 2 75 | 16.82 15.51 0.8786 6.017 3.486 4.004 5.841 2 76 | 16.77 15.62 0.8638 5.927 3.438 4.92 5.795 2 77 | 17.32 15.91 0.8599 6.064 3.403 3.824 5.922 2 78 | 20.71 17.23 0.8763 6.579 3.814 4.451 6.451 2 79 | 18.94 16.49 0.875 6.445 3.639 5.064 6.362 2 80 | 17.12 15.55 0.8892 5.85 3.566 2.858 5.746 2 81 | 16.53 15.34 0.8823 5.875 3.467 5.532 5.88 2 82 | 18.72 16.19 0.8977 6.006 3.857 5.324 5.879 2 83 | 20.2 16.89 0.8894 6.285 3.864 5.173 6.187 2 84 | 19.57 16.74 0.8779 6.384 3.772 1.472 6.273 2 85 | 19.51 16.71 0.878 6.366 3.801 2.962 6.185 2 86 | 18.27 16.09 0.887 6.173 3.651 2.443 6.197 2 87 | 18.88 16.26 0.8969 6.084 3.764 1.649 6.109 2 88 | 18.98 16.66 0.859 6.549 3.67 3.691 6.498 2 89 | 21.18 17.21 0.8989 6.573 4.033 5.78 6.231 2 90 | 20.88 17.05 0.9031 6.45 4.032 5.016 6.321 2 91 | 20.1 16.99 0.8746 6.581 3.785 1.955 6.449 2 92 | 18.76 16.2 0.8984 6.172 3.796 3.12 6.053 2 93 | 18.81 16.29 0.8906 6.272 3.693 3.237 6.053 2 94 | 18.59 16.05 0.9066 6.037 3.86 6.001 5.877 2 95 | 18.36 16.52 0.8452 6.666 3.485 4.933 6.448 2 96 | 16.87 15.65 0.8648 6.139 3.463 3.696 5.967 2 97 | 19.31 16.59 0.8815 6.341 3.81 3.477 6.238 2 98 | 18.98 16.57 0.8687 6.449 3.552 2.144 6.453 2 99 | 18.17 16.26 0.8637 6.271 3.512 2.853 6.273 2 100 | 18.72 16.34 0.881 6.219 3.684 2.188 6.097 2 101 | 16.41 15.25 0.8866 5.718 3.525 4.217 5.618 2 102 | 17.99 15.86 0.8992 5.89 3.694 2.068 5.837 2 103 | 19.46 16.5 0.8985 6.113 3.892 4.308 6.009 2 104 | 19.18 16.63 0.8717 6.369 3.681 3.357 6.229 2 105 | 18.95 16.42 0.8829 6.248 3.755 3.368 6.148 2 106 | 18.83 16.29 0.8917 6.037 3.786 2.553 5.879 2 107 | 18.85 16.17 0.9056 6.152 3.806 2.843 6.2 2 108 | 17.63 15.86 0.88 6.033 3.573 3.747 5.929 2 109 | 19.94 16.92 0.8752 6.675 3.763 3.252 6.55 2 110 | 18.55 16.22 0.8865 6.153 3.674 1.738 5.894 2 111 | 18.45 16.12 0.8921 6.107 3.769 2.235 5.794 2 112 | 19.38 16.72 0.8716 6.303 3.791 3.678 5.965 2 113 | 19.13 16.31 0.9035 6.183 3.902 2.109 5.924 2 114 | 19.14 16.61 0.8722 6.259 3.737 6.682 6.053 2 115 | 20.97 17.25 0.8859 6.563 3.991 4.677 6.316 2 116 | 19.06 16.45 0.8854 6.416 3.719 2.248 6.163 2 117 | 18.96 16.2 0.9077 6.051 3.897 4.334 5.75 2 118 | 19.15 16.45 0.889 6.245 3.815 3.084 6.185 2 119 | 18.89 16.23 0.9008 6.227 3.769 3.639 5.966 2 120 | 20.03 16.9 0.8811 6.493 3.857 3.063 6.32 2 121 | 20.24 16.91 0.8897 6.315 3.962 5.901 6.188 2 122 | 18.14 16.12 0.8772 6.059 3.563 3.619 6.011 2 123 | 16.17 15.38 0.8588 5.762 3.387 4.286 5.703 2 124 | 18.43 15.97 0.9077 5.98 3.771 2.984 5.905 2 125 | 15.99 14.89 0.9064 5.363 3.582 3.336 5.144 2 126 | 18.75 16.18 0.8999 6.111 3.869 4.188 5.992 2 127 | 18.65 16.41 0.8698 6.285 3.594 4.391 6.102 2 128 | 17.98 15.85 0.8993 5.979 3.687 2.257 5.919 2 129 | 20.16 17.03 0.8735 6.513 3.773 1.91 6.185 2 130 | 17.55 15.66 0.8991 5.791 3.69 5.366 5.661 2 131 | 18.3 15.89 0.9108 5.979 3.755 2.837 5.962 2 132 | 18.94 16.32 0.8942 6.144 3.825 2.908 5.949 2 133 | 15.38 14.9 0.8706 5.884 3.268 4.462 5.795 2 134 | 16.16 15.33 0.8644 5.845 3.395 4.266 5.795 2 135 | 15.56 14.89 0.8823 5.776 3.408 4.972 5.847 2 136 | 15.38 14.66 0.899 5.477 3.465 3.6 5.439 2 137 | 17.36 15.76 0.8785 6.145 3.574 3.526 5.971 2 138 | 15.57 15.15 0.8527 5.92 3.231 2.64 5.879 2 139 | 15.6 15.11 0.858 5.832 3.286 2.725 5.752 2 140 | 16.23 15.18 0.885 5.872 3.472 3.769 5.922 2 141 | 13.07 13.92 0.848 5.472 2.994 5.304 5.395 3 142 | 13.32 13.94 0.8613 5.541 3.073 7.035 5.44 3 143 | 13.34 13.95 0.862 5.389 3.074 5.995 5.307 3 144 | 12.22 13.32 0.8652 5.224 2.967 5.469 5.221 3 145 | 11.82 13.4 0.8274 5.314 2.777 4.471 5.178 3 146 | 11.21 13.13 0.8167 5.279 2.687 6.169 5.275 3 147 | 11.43 13.13 0.8335 5.176 2.719 2.221 5.132 3 148 | 12.49 13.46 0.8658 5.267 2.967 4.421 5.002 3 149 | 12.7 13.71 0.8491 5.386 2.911 3.26 5.316 3 150 | 10.79 12.93 0.8107 5.317 2.648 5.462 5.194 3 151 | 11.83 13.23 0.8496 5.263 2.84 5.195 5.307 3 152 | 12.01 13.52 0.8249 5.405 2.776 6.992 5.27 3 153 | 12.26 13.6 0.8333 5.408 2.833 4.756 5.36 3 154 | 11.18 13.04 0.8266 5.22 2.693 3.332 5.001 3 155 | 11.36 13.05 0.8382 5.175 2.755 4.048 5.263 3 156 | 11.19 13.05 0.8253 5.25 2.675 5.813 5.219 3 157 | 11.34 12.87 0.8596 5.053 2.849 3.347 5.003 3 158 | 12.13 13.73 0.8081 5.394 2.745 4.825 5.22 3 159 | 11.75 13.52 0.8082 5.444 2.678 4.378 5.31 3 160 | 11.49 13.22 0.8263 5.304 2.695 5.388 5.31 3 161 | 12.54 13.67 0.8425 5.451 2.879 3.082 5.491 3 162 | 12.02 13.33 0.8503 5.35 2.81 4.271 5.308 3 163 | 12.05 13.41 0.8416 5.267 2.847 4.988 5.046 3 164 | 12.55 13.57 0.8558 5.333 2.968 4.419 5.176 3 165 | 11.14 12.79 0.8558 5.011 2.794 6.388 5.049 3 166 | 12.1 13.15 0.8793 5.105 2.941 2.201 5.056 3 167 | 12.44 13.59 0.8462 5.319 2.897 4.924 5.27 3 168 | 12.15 13.45 0.8443 5.417 2.837 3.638 5.338 3 169 | 11.35 13.12 0.8291 5.176 2.668 4.337 5.132 3 170 | 11.24 13 0.8359 5.09 2.715 3.521 5.088 3 171 | 11.02 13 0.8189 5.325 2.701 6.735 5.163 3 172 | 11.55 13.1 0.8455 5.167 2.845 6.715 4.956 3 173 | 11.27 12.97 0.8419 5.088 2.763 4.309 5 3 174 | 11.4 13.08 0.8375 5.136 2.763 5.588 5.089 3 175 | 10.83 12.96 0.8099 5.278 2.641 5.182 5.185 3 176 | 10.8 12.57 0.859 4.981 2.821 4.773 5.063 3 177 | 11.26 13.01 0.8355 5.186 2.71 5.335 5.092 3 178 | 10.74 12.73 0.8329 5.145 2.642 4.702 4.963 3 179 | 11.48 13.05 0.8473 5.18 2.758 5.876 5.002 3 180 | 12.21 13.47 0.8453 5.357 2.893 1.661 5.178 3 181 | 11.41 12.95 0.856 5.09 2.775 4.957 4.825 3 182 | 12.46 13.41 0.8706 5.236 3.017 4.987 5.147 3 183 | 12.19 13.36 0.8579 5.24 2.909 4.857 5.158 3 184 | 11.65 13.07 0.8575 5.108 2.85 5.209 5.135 3 185 | 12.89 13.77 0.8541 5.495 3.026 6.185 5.316 3 186 | 11.56 13.31 0.8198 5.363 2.683 4.062 5.182 3 187 | 11.81 13.45 0.8198 5.413 2.716 4.898 5.352 3 188 | 10.91 12.8 0.8372 5.088 2.675 4.179 4.956 3 189 | 11.23 12.82 0.8594 5.089 2.821 7.524 4.957 3 190 | 10.59 12.41 0.8648 4.899 2.787 4.975 4.794 3 191 | 10.93 12.8 0.839 5.046 2.717 5.398 5.045 3 192 | 11.27 12.86 0.8563 5.091 2.804 3.985 5.001 3 193 | 11.87 13.02 0.8795 5.132 2.953 3.597 5.132 3 194 | 10.82 12.83 0.8256 5.18 2.63 4.853 5.089 3 195 | 12.11 13.27 0.8639 5.236 2.975 4.132 5.012 3 196 | 12.8 13.47 0.886 5.16 3.126 4.873 4.914 3 197 | 12.79 13.53 0.8786 5.224 3.054 5.483 4.958 3 198 | 13.37 13.78 0.8849 5.32 3.128 4.67 5.091 3 199 | 12.62 13.67 0.8481 5.41 2.911 3.306 5.231 3 200 | 12.76 13.38 0.8964 5.073 3.155 2.828 4.83 3 201 | 12.38 13.44 0.8609 5.219 2.989 5.472 5.045 3 202 | 12.67 13.32 0.8977 4.984 3.135 2.3 4.745 3 203 | 11.18 12.72 0.868 5.009 2.81 4.051 4.828 3 204 | 12.7 13.41 0.8874 5.183 3.091 8.456 5 3 205 | 12.37 13.47 0.8567 5.204 2.96 3.919 5.001 3 206 | 12.19 13.2 0.8783 5.137 2.981 3.631 4.87 3 207 | 11.23 12.88 0.8511 5.14 2.795 4.325 5.003 3 208 | 13.2 13.66 0.8883 5.236 3.232 8.315 5.056 3 209 | 11.84 13.21 0.8521 5.175 2.836 3.598 5.044 3 210 | 12.3 13.34 0.8684 5.243 2.974 5.637 5.063 3 -------------------------------------------------------------------------------- /Chapter08/yacht_hydrodynamics.csv: -------------------------------------------------------------------------------- 1 | -2.3 0.568 4.78 3.99 3.17 0.125 0.11 2 | -2.3 0.568 4.78 3.99 3.17 0.150 0.27 3 | -2.3 0.568 4.78 3.99 3.17 0.175 0.47 4 | -2.3 0.568 4.78 3.99 3.17 0.200 0.78 5 | -2.3 0.568 4.78 3.99 3.17 0.225 1.18 6 | -2.3 0.568 4.78 3.99 3.17 0.250 1.82 7 | -2.3 0.568 4.78 3.99 3.17 0.275 2.61 8 | -2.3 0.568 4.78 3.99 3.17 0.300 3.76 9 | -2.3 0.568 4.78 3.99 3.17 0.325 4.99 10 | -2.3 0.568 4.78 3.99 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3.4413690e+00 2.9887198e+00 1800 | 3.4626560e+00 2.9295542e+00 1801 | 3.4001760e+00 2.9826256e+00 1802 | 3.4181080e+00 2.9690495e+00 1803 | 3.4758780e+00 3.0341755e+00 1804 | 3.4214880e+00 3.0528795e+00 1805 | 3.3998290e+00 2.9732393e+00 1806 | 3.3981240e+00 3.0530880e+00 1807 | 3.5039770e+00 3.0198461e+00 1808 | 3.3601640e+00 3.0317012e+00 1809 | 3.4778920e+00 2.9497316e+00 1810 | 3.4939660e+00 3.0482098e+00 1811 | 3.3963220e+00 3.0147407e+00 1812 | 3.4620080e+00 3.0310257e+00 1813 | 3.4074650e+00 2.9379680e+00 1814 | 3.3237450e+00 2.9893051e+00 1815 | 3.3412540e+00 2.9435743e+00 1816 | 3.3934860e+00 2.9913926e+00 1817 | 3.4261300e+00 3.0450241e+00 1818 | 3.4198520e+00 2.9595560e+00 1819 | 3.3629980e+00 3.0062123e+00 1820 | 3.4353260e+00 2.9933973e+00 1821 | 3.3749860e+00 2.9665453e+00 1822 | 3.3277810e+00 2.9343906e+00 1823 | 3.3571640e+00 2.9803027e+00 1824 | 3.4011740e+00 2.9809237e+00 1825 | 3.4031330e+00 2.9898422e+00 1826 | 3.3622320e+00 3.0126663e+00 1827 | 3.4134440e+00 2.9894913e+00 1828 | 3.3546300e+00 2.9746431e+00 1829 | 3.4443530e+00 2.9483740e+00 1830 | 3.3483830e+00 3.0003349e+00 1831 | 3.3188530e+00 3.0782789e+00 1832 | 3.3176640e+00 2.9710835e+00 1833 | 3.3776780e+00 2.9928570e+00 1834 | 3.3668930e+00 3.0349842e+00 1835 | 3.3896450e+00 3.1017170e+00 1836 | 3.3229030e+00 3.0123926e+00 1837 | 3.3619660e+00 3.0584713e+00 1838 | 3.3758360e+00 2.9982198e+00 1839 | 3.4028510e+00 3.0095437e+00 1840 | 3.3430280e+00 3.0061611e+00 1841 | 3.3680950e+00 3.0802440e+00 1842 | 3.3482760e+00 3.0107296e+00 1843 | 3.3952700e+00 3.0502951e+00 1844 | 3.3391820e+00 3.0271601e+00 1845 | 3.2994810e+00 3.0083343e+00 1846 | 3.3434210e+00 3.0240705e+00 1847 | 3.4135400e+00 3.1440630e+00 1848 | 3.3630960e+00 3.0467992e+00 1849 | 3.3321170e+00 3.0647559e+00 1850 | 3.3458000e+00 3.1206760e+00 1851 | 3.3775160e+00 3.1267910e+00 1852 | 3.3246320e+00 3.1058700e+00 1853 | 3.3828100e+00 3.1192000e+00 1854 | 3.4024940e+00 3.1439930e+00 1855 | 3.3514000e+00 3.0752348e+00 1856 | 3.3441080e+00 3.0989193e+00 1857 | 3.3563960e+00 3.1484470e+00 1858 | 3.3178470e+00 3.1125080e+00 1859 | 3.3106250e+00 3.1520070e+00 1860 | 3.3426560e+00 3.1341440e+00 1861 | 3.3781810e+00 3.1421630e+00 1862 | 3.3930200e+00 3.1756930e+00 1863 | 3.2328580e+00 3.1683040e+00 1864 | 3.3715980e+00 3.1130460e+00 1865 | 3.3532650e+00 3.1585780e+00 1866 | 3.2905380e+00 3.1129940e+00 1867 | 3.3365280e+00 3.1161400e+00 1868 | 3.3659250e+00 3.0398461e+00 1869 | 3.3383470e+00 3.0980892e+00 1870 | 3.3701980e+00 3.1330330e+00 1871 | 3.3593520e+00 3.0893825e+00 1872 | 3.3747220e+00 3.1354680e+00 1873 | 3.3734100e+00 3.1801830e+00 1874 | 3.3375900e+00 3.1993080e+00 1875 | 3.3418520e+00 3.1444450e+00 1876 | 3.4322070e+00 3.1034550e+00 1877 | 3.3178390e+00 3.1287770e+00 1878 | 3.3702700e+00 3.1874100e+00 1879 | 3.3427660e+00 3.1829890e+00 1880 | 3.3679160e+00 3.1311490e+00 1881 | 3.2944890e+00 3.1699420e+00 1882 | 3.4131440e+00 3.1437770e+00 1883 | 3.4280380e+00 3.1425350e+00 1884 | 3.3501620e+00 3.2072580e+00 1885 | 3.4151080e+00 3.1353430e+00 1886 | 3.3521800e+00 3.0665528e+00 1887 | 3.4157500e+00 3.1592900e+00 1888 | 3.3600030e+00 3.2025040e+00 1889 | 3.3561250e+00 3.1957730e+00 1890 | 3.3579630e+00 3.1991000e+00 1891 | 3.3585920e+00 3.0272017e+00 1892 | 3.4204450e+00 3.2064240e+00 1893 | 3.3561790e+00 3.1868630e+00 1894 | 3.3930570e+00 3.1332380e+00 1895 | 3.4371520e+00 3.0678852e+00 1896 | 3.3725180e+00 3.0752175e+00 1897 | 3.3420450e+00 3.1009200e+00 1898 | 3.3909960e+00 3.1347580e+00 1899 | 3.4047590e+00 3.1287930e+00 1900 | 3.3734720e+00 3.1419720e+00 1901 | 3.4529710e+00 3.0280604e+00 1902 | 3.4520740e+00 3.0647822e+00 1903 | 3.5206540e+00 3.0138009e+00 1904 | 3.5086370e+00 3.0788784e+00 1905 | 3.3901520e+00 3.0289296e+00 1906 | 3.4261630e+00 3.0953231e+00 1907 | 3.3525200e+00 3.0435456e+00 1908 | 3.4217390e+00 2.9870164e+00 1909 | 3.4537250e+00 3.0142706e+00 1910 | 3.4728920e+00 3.1264670e+00 1911 | 3.5724740e+00 3.0411965e+00 1912 | 3.4805540e+00 3.0385064e+00 1913 | 3.4588820e+00 2.9957480e+00 1914 | 3.5041690e+00 3.0736425e+00 1915 | 3.4934040e+00 3.0295427e+00 1916 | 3.5219070e+00 3.0623684e+00 1917 | 3.5414390e+00 3.0576602e+00 1918 | 3.5012160e+00 3.0092359e+00 1919 | 3.5099420e+00 3.0521624e+00 1920 | 3.5291600e+00 3.0288131e+00 1921 | 3.6000320e+00 3.0294187e+00 1922 | 3.6339400e+00 3.0908945e+00 1923 | 3.5251120e+00 3.0014378e+00 1924 | 3.6267290e+00 3.0566602e+00 1925 | 3.5584050e+00 3.0157959e+00 1926 | 3.5254550e+00 3.0033830e+00 1927 | 3.5482530e+00 3.0343131e+00 1928 | 3.5705310e+00 3.0770846e+00 1929 | 3.5158980e+00 3.0077502e+00 1930 | 3.6076030e+00 3.0304182e+00 1931 | 3.5972780e+00 3.0062839e+00 1932 | 3.6751440e+00 3.0467155e+00 1933 | 3.6952130e+00 2.9812772e+00 1934 | 3.6127180e+00 3.0348131e+00 1935 | 3.6743830e+00 3.0008840e+00 1936 | 3.5627600e+00 2.9862061e+00 1937 | 3.6030490e+00 3.0278711e+00 1938 | 3.6050380e+00 2.9567377e+00 1939 | 3.7194040e+00 2.9834811e+00 1940 | 3.6363420e+00 2.9635104e+00 1941 | 3.6384160e+00 2.9116318e+00 1942 | 3.7308220e+00 3.0413414e+00 1943 | 3.6756500e+00 2.9661508e+00 1944 | 3.6697300e+00 2.9936301e+00 1945 | 3.7110920e+00 3.0072824e+00 1946 | 3.7293180e+00 3.0041832e+00 1947 | 3.6836590e+00 2.9905079e+00 1948 | 3.7342060e+00 2.9825614e+00 1949 | 3.6456320e+00 2.9962481e+00 1950 | 3.6577330e+00 2.9447373e+00 1951 | 3.7653520e+00 3.0172320e+00 1952 | 3.7915020e+00 3.0140673e+00 1953 | 3.7471990e+00 2.9435554e+00 1954 | 3.7738910e+00 2.9934610e+00 1955 | 3.7670220e+00 2.9866180e+00 1956 | 3.6877570e+00 3.0098695e+00 1957 | 3.7416670e+00 2.9609518e+00 1958 | 3.7633280e+00 2.9556639e+00 1959 | 3.6978790e+00 3.0398184e+00 1960 | 3.7338650e+00 3.0086816e+00 1961 | 3.7874110e+00 3.0334330e+00 1962 | 3.7965770e+00 2.9818920e+00 1963 | 3.8467350e+00 2.9851957e+00 1964 | 3.8461020e+00 2.9728166e+00 1965 | 3.7080850e+00 2.9994472e+00 1966 | 3.7115170e+00 2.9749104e+00 1967 | 3.7686210e+00 3.0041134e+00 1968 | 3.8010970e+00 2.9573957e+00 1969 | 3.7731240e+00 3.0404654e+00 1970 | 3.7318070e+00 2.9692805e+00 1971 | 3.8203380e+00 2.9223897e+00 1972 | 3.8185630e+00 3.0192971e+00 1973 | 3.8295820e+00 2.9441451e+00 1974 | 3.8692370e+00 2.9267813e+00 1975 | 3.8474930e+00 2.9838045e+00 1976 | 3.8655810e+00 3.0344461e+00 1977 | 3.8177120e+00 2.9747930e+00 1978 | 3.7869800e+00 3.0059132e+00 1979 | 3.8023820e+00 3.0407809e+00 1980 | 3.7736190e+00 2.9694627e+00 1981 | 3.8348170e+00 2.9763104e+00 1982 | 3.8491630e+00 2.9843417e+00 1983 | 3.8086660e+00 2.9406334e+00 1984 | 3.7854030e+00 2.9722152e+00 1985 | 3.8275730e+00 2.9211476e+00 1986 | 3.8643330e+00 3.0199008e+00 1987 | 3.8772090e+00 3.0418126e+00 1988 | 3.7812940e+00 2.9434723e+00 1989 | 3.8424630e+00 3.0810089e+00 1990 | 3.8352630e+00 3.0265340e+00 1991 | 3.9077800e+00 3.0945422e+00 1992 | 3.8507690e+00 3.0805453e+00 1993 | 3.8240140e+00 3.0741858e+00 1994 | 3.9483550e+00 2.9783629e+00 1995 | 3.8656200e+00 3.1115640e+00 1996 | 3.8174020e+00 3.0956774e+00 1997 | 3.8565970e+00 3.1062910e+00 1998 | 3.8530570e+00 3.0768458e+00 1999 | 3.8671290e+00 3.0985013e+00 2000 | 3.8657640e+00 3.0176486e+00 2001 | 3.8970160e+00 3.1217520e+00 2002 | 3.9133090e+00 3.0939948e+00 2003 | 3.8570030e+00 3.0815059e+00 2004 | 3.7860730e+00 3.1589140e+00 2005 | 3.8818700e+00 3.1628660e+00 2006 | 3.9110660e+00 3.0717377e+00 2007 | 3.9035960e+00 3.0643444e+00 2008 | 3.8514420e+00 3.1440660e+00 2009 | 3.8419800e+00 3.1496970e+00 2010 | 3.8712060e+00 3.1524040e+00 2011 | 3.8800870e+00 3.1566480e+00 2012 | 3.8491800e+00 3.1062000e+00 2013 | 3.9285700e+00 3.1384370e+00 2014 | 3.8733670e+00 3.0982565e+00 2015 | 3.9263040e+00 3.1490350e+00 2016 | 3.9128070e+00 3.0531126e+00 2017 | 3.8882080e+00 3.1613960e+00 2018 | 3.9327710e+00 3.0621851e+00 2019 | 3.8513900e+00 3.0447351e+00 2020 | 3.9250560e+00 3.1601900e+00 2021 | 3.9251330e+00 3.0514875e+00 2022 | 3.9138920e+00 3.1776870e+00 2023 | 3.8634210e+00 3.1518490e+00 2024 | 3.8470390e+00 3.1787730e+00 2025 | 3.8598460e+00 3.1018530e+00 2026 | 3.8780410e+00 3.1772310e+00 2027 | 3.8378540e+00 3.1387500e+00 2028 | 3.9015970e+00 3.2407550e+00 2029 | 3.8492990e+00 3.1244370e+00 2030 | 3.9055040e+00 3.1427790e+00 2031 | 3.8590250e+00 3.1499710e+00 2032 | 3.9448380e+00 3.1812410e+00 2033 | 3.8879470e+00 3.1720090e+00 2034 | 3.9009740e+00 3.2101810e+00 2035 | 3.9006610e+00 3.2080710e+00 2036 | 3.9180480e+00 3.1617730e+00 2037 | 3.8984230e+00 3.1944840e+00 2038 | 3.9165490e+00 3.2297570e+00 2039 | 3.8681120e+00 3.1861810e+00 2040 | 3.9199500e+00 3.1636910e+00 2041 | 3.8920770e+00 3.2200780e+00 2042 | 3.8981530e+00 3.2141030e+00 2043 | 3.8757800e+00 3.1643540e+00 2044 | 3.8494560e+00 3.2409580e+00 2045 | 3.9289780e+00 3.1507730e+00 2046 | 3.8833910e+00 3.2762620e+00 2047 | 3.9618100e+00 3.2428800e+00 2048 | 3.8888640e+00 3.1664540e+00 2049 | 3.8720540e+00 3.2128440e+00 2050 | 3.9894830e+00 3.1736620e+00 2051 | 3.8339750e+00 3.2420160e+00 2052 | 3.9380930e+00 3.2637730e+00 2053 | 3.9324490e+00 3.2841340e+00 2054 | 3.8981840e+00 3.2885740e+00 2055 | 3.9824210e+00 3.2578600e+00 2056 | 3.9355260e+00 3.2272540e+00 2057 | 3.8763280e+00 3.2709610e+00 2058 | 3.8969800e+00 3.2842150e+00 2059 | 3.8930780e+00 3.2332860e+00 2060 | 3.8985230e+00 3.2417210e+00 2061 | 3.8925860e+00 3.2921900e+00 2062 | 3.9014370e+00 3.2084450e+00 2063 | 3.8951810e+00 3.2328560e+00 2064 | 3.9292020e+00 3.2543830e+00 2065 | 3.8786780e+00 3.2168990e+00 2066 | 3.9333930e+00 3.3280480e+00 2067 | 3.8670800e+00 3.3335390e+00 2068 | 3.9635970e+00 3.2389620e+00 2069 | 3.8957920e+00 3.2661530e+00 2070 | 3.8445150e+00 3.2267890e+00 2071 | 3.8540850e+00 3.3384270e+00 2072 | 3.8870870e+00 3.3405730e+00 2073 | 3.8853970e+00 3.2944400e+00 2074 | 3.9377270e+00 3.3491730e+00 2075 | 3.8466040e+00 3.2845420e+00 2076 | 3.8533380e+00 3.4053960e+00 2077 | 3.7906450e+00 3.3731860e+00 2078 | 3.8463450e+00 3.3176770e+00 2079 | 3.8866300e+00 3.2249630e+00 2080 | 3.8226480e+00 3.2410320e+00 2081 | 3.7898430e+00 3.2330220e+00 2082 | 3.8060440e+00 3.2564720e+00 2083 | 3.7725670e+00 3.2333750e+00 2084 | 3.7775090e+00 3.2915610e+00 2085 | 3.7901390e+00 3.3031340e+00 2086 | 3.8363600e+00 3.2948790e+00 2087 | 3.7733610e+00 3.2908380e+00 2088 | 3.8012490e+00 3.3049860e+00 2089 | 3.7768430e+00 3.2867090e+00 2090 | 3.7876110e+00 3.3227490e+00 2091 | 3.6213580e+00 3.2849110e+00 2092 | 3.6577990e+00 3.3253490e+00 2093 | 3.7149070e+00 3.2497830e+00 2094 | 3.6894490e+00 3.2942480e+00 2095 | 3.7043110e+00 3.2616080e+00 2096 | 3.6593990e+00 3.2295210e+00 2097 | 3.7019360e+00 3.3245990e+00 2098 | 3.6763820e+00 3.2371580e+00 2099 | 3.6189180e+00 3.3068940e+00 2100 | 3.6851250e+00 3.2261890e+00 2101 | 3.6049210e+00 3.2673970e+00 2102 | 3.6323260e+00 3.2740180e+00 2103 | 3.6477040e+00 3.2929810e+00 2104 | 3.6355600e+00 3.1949470e+00 2105 | 3.6030970e+00 3.2171880e+00 2106 | 3.6758430e+00 3.2297160e+00 2107 | 3.5499300e+00 3.2476250e+00 2108 | 3.6235370e+00 3.1805070e+00 2109 | 3.6353150e+00 3.2777490e+00 2110 | 3.5445020e+00 3.1689320e+00 2111 | 3.5593730e+00 3.1741500e+00 2112 | 3.5562240e+00 3.2477650e+00 2113 | 3.5226160e+00 3.2096140e+00 2114 | 3.5846340e+00 3.2362850e+00 2115 | 3.5274510e+00 3.2476660e+00 2116 | 3.5465680e+00 3.1937280e+00 2117 | 3.5103970e+00 3.2246400e+00 2118 | 3.5341800e+00 3.2592230e+00 2119 | 3.6112030e+00 3.1859810e+00 2120 | 3.5957500e+00 3.2050460e+00 2121 | 3.4224970e+00 3.1811100e+00 2122 | 3.4481010e+00 3.2169840e+00 2123 | 3.4599080e+00 3.2420740e+00 2124 | 3.4283230e+00 3.2302850e+00 2125 | 3.4828510e+00 3.2014330e+00 2126 | 3.3890550e+00 3.2711320e+00 2127 | 3.4557110e+00 3.2426290e+00 2128 | 3.4592510e+00 3.1790020e+00 2129 | 3.4709790e+00 3.1201600e+00 2130 | 3.4704360e+00 3.1649620e+00 2131 | 3.5128020e+00 3.2148750e+00 2132 | 3.4516610e+00 3.2114140e+00 2133 | 3.4203530e+00 3.2268580e+00 2134 | 3.4698090e+00 3.1863200e+00 2135 | 3.5098110e+00 3.2057030e+00 2136 | 3.5017380e+00 3.1424940e+00 2137 | 3.4585500e+00 3.2259800e+00 2138 | 3.5017280e+00 3.1371960e+00 2139 | 3.4897980e+00 3.2255360e+00 2140 | 3.4082600e+00 3.1976510e+00 2141 | 3.4466080e+00 3.2298040e+00 2142 | 3.4770730e+00 3.1648260e+00 2143 | 3.4814360e+00 3.2169470e+00 2144 | 3.4537850e+00 3.1578070e+00 2145 | 3.4408850e+00 3.1650980e+00 2146 | 3.5148050e+00 3.2668270e+00 2147 | 3.4892450e+00 3.1416950e+00 2148 | 3.4807500e+00 3.1534310e+00 2149 | 3.4785880e+00 3.1717520e+00 2150 | 3.5047500e+00 3.1518370e+00 2151 | 3.5061210e+00 3.1662990e+00 2152 | 3.5666750e+00 3.0844868e+00 2153 | 3.5598340e+00 3.2023530e+00 2154 | 3.5604780e+00 3.1616580e+00 2155 | 3.5286490e+00 3.0987349e+00 2156 | 3.5010730e+00 3.2246290e+00 2157 | 3.4343070e+00 3.1424850e+00 2158 | 3.5346970e+00 3.1617310e+00 2159 | 3.4763060e+00 3.1395540e+00 2160 | 3.5639940e+00 3.1448130e+00 2161 | 3.5512280e+00 3.0949754e+00 2162 | 3.5385610e+00 3.1031230e+00 2163 | 3.5280070e+00 3.0655343e+00 2164 | 3.5275180e+00 3.1593220e+00 2165 | 3.5886450e+00 3.0906431e+00 2166 | 3.5799450e+00 3.1370880e+00 2167 | 3.6133290e+00 3.0613277e+00 2168 | 3.5507320e+00 3.1981290e+00 2169 | 3.4706580e+00 3.1617530e+00 2170 | 3.5217420e+00 3.1017740e+00 2171 | 3.6593580e+00 3.1018860e+00 2172 | 3.6123860e+00 3.0644705e+00 2173 | 3.6385320e+00 3.0686981e+00 2174 | 3.6298150e+00 3.1048840e+00 2175 | 3.6204300e+00 3.0169883e+00 2176 | 3.5896160e+00 3.0276677e+00 2177 | 3.6057870e+00 3.0520037e+00 2178 | 3.6316520e+00 3.0203947e+00 2179 | 3.6814870e+00 3.0483083e+00 2180 | 3.6342880e+00 3.0484595e+00 2181 | 3.7474340e+00 2.9986200e+00 2182 | 3.7317940e+00 3.0519302e+00 2183 | 3.7487400e+00 3.0489286e+00 2184 | 3.7100990e+00 3.0541471e+00 2185 | 3.6868070e+00 3.0828672e+00 2186 | 3.6814600e+00 3.0871006e+00 2187 | 3.7493700e+00 3.0559754e+00 2188 | 3.6675450e+00 3.0275056e+00 2189 | 3.7679500e+00 3.0632320e+00 2190 | 3.7295870e+00 3.0825802e+00 2191 | 3.7537560e+00 3.0103314e+00 2192 | 3.7430560e+00 3.0759108e+00 2193 | 3.7763950e+00 3.1214300e+00 2194 | 3.8270190e+00 3.0557480e+00 2195 | 3.7803710e+00 3.0575365e+00 2196 | 3.7907200e+00 2.9957396e+00 2197 | 3.7836560e+00 3.0639863e+00 2198 | 3.7840690e+00 3.0540888e+00 2199 | 3.6583440e+00 3.0893481e+00 2200 | 3.7189830e+00 3.1076200e+00 2201 | 3.8729460e+00 3.0075823e+00 2202 | 3.7960630e+00 2.9752256e+00 2203 | 3.8057860e+00 3.1001170e+00 2204 | 3.8170150e+00 3.0824213e+00 2205 | 3.7874590e+00 2.9884472e+00 2206 | 3.8196400e+00 3.0346005e+00 2207 | 3.8301680e+00 3.0876462e+00 2208 | 3.8271080e+00 3.0532686e+00 2209 | 3.7993800e+00 3.0455109e+00 2210 | 3.8404500e+00 3.0361905e+00 2211 | 3.7842050e+00 3.1477310e+00 2212 | 3.8961480e+00 3.1334130e+00 2213 | 3.8624780e+00 3.1414940e+00 2214 | 3.8143150e+00 3.0714330e+00 2215 | 3.8627810e+00 3.1577520e+00 2216 | 3.9402890e+00 3.0830954e+00 2217 | 3.8587770e+00 3.0680663e+00 2218 | 3.8406450e+00 3.1216740e+00 2219 | 3.8462800e+00 3.0933100e+00 2220 | 3.8795770e+00 3.0695410e+00 2221 | 3.8412690e+00 3.1450380e+00 2222 | 3.8903750e+00 3.1589860e+00 2223 | 3.8839550e+00 3.2038810e+00 2224 | 3.8916920e+00 3.1550970e+00 2225 | 3.7930670e+00 3.1479040e+00 2226 | 3.8744720e+00 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2.5358080e+00 4108 | 3.7837050e+00 2.6085990e+00 4109 | 3.8586580e+00 2.6041870e+00 4110 | 3.8619280e+00 2.5605000e+00 4111 | 3.8981050e+00 2.5432430e+00 4112 | 3.8829240e+00 2.6121500e+00 4113 | 3.7911610e+00 2.5756760e+00 4114 | 3.7887930e+00 2.5570530e+00 4115 | 3.8103790e+00 2.5955470e+00 4116 | 3.8374090e+00 2.5779820e+00 4117 | 3.8699270e+00 2.5936620e+00 4118 | 3.8071490e+00 2.6257800e+00 4119 | 3.8182990e+00 2.5803010e+00 4120 | 3.7849960e+00 2.6855430e+00 4121 | 3.8257390e+00 2.5023750e+00 4122 | 3.8546540e+00 2.6004560e+00 4123 | 3.8979580e+00 2.5978670e+00 4124 | 3.8153200e+00 2.5643740e+00 4125 | 3.8521920e+00 2.5789710e+00 4126 | 3.8476350e+00 2.5375530e+00 4127 | 3.8611850e+00 2.4667300e+00 4128 | 3.8790670e+00 2.5767050e+00 4129 | 3.8508380e+00 2.5683490e+00 4130 | 3.8228480e+00 2.6443750e+00 4131 | 3.8174000e+00 2.5746820e+00 4132 | 3.7526890e+00 2.5595360e+00 4133 | 3.8984320e+00 2.5877570e+00 4134 | 3.7507840e+00 2.5511900e+00 4135 | 3.8621570e+00 2.5976160e+00 4136 | 3.8848940e+00 2.5928670e+00 4137 | 3.7657730e+00 2.5868650e+00 4138 | 3.8370620e+00 2.5739570e+00 4139 | 3.8407280e+00 2.5573900e+00 4140 | 3.8017230e+00 2.6063220e+00 4141 | 3.7716930e+00 2.5362020e+00 4142 | 3.8561520e+00 2.5800340e+00 4143 | 3.7892810e+00 2.5017530e+00 4144 | 3.8322170e+00 2.6236810e+00 4145 | 3.7184610e+00 2.5577650e+00 4146 | 3.7118620e+00 2.5383180e+00 4147 | 3.8006420e+00 2.6333070e+00 4148 | 3.8400580e+00 2.5930180e+00 4149 | 3.7572800e+00 2.5612610e+00 4150 | 3.7725450e+00 2.6440830e+00 4151 | 3.8474620e+00 2.5291880e+00 4152 | 3.7817660e+00 2.5907890e+00 4153 | 3.7989630e+00 2.5952250e+00 4154 | 3.8131490e+00 2.6222490e+00 4155 | 3.8546680e+00 2.5404340e+00 4156 | 3.8183030e+00 2.5828460e+00 4157 | 3.8304960e+00 2.5330380e+00 4158 | 3.7921720e+00 2.5286700e+00 4159 | 3.7818010e+00 2.5714770e+00 4160 | 3.7973470e+00 2.6304870e+00 4161 | 3.7793360e+00 2.5487850e+00 4162 | 3.7874770e+00 2.5614260e+00 4163 | 3.7766010e+00 2.5003390e+00 4164 | 3.8096000e+00 2.4940070e+00 4165 | 3.7685160e+00 2.6179530e+00 4166 | 3.8278790e+00 2.5404640e+00 4167 | 3.7445660e+00 2.5287990e+00 4168 | 3.8110920e+00 2.6131100e+00 4169 | 3.8382320e+00 2.5726610e+00 4170 | 3.8237010e+00 2.5619920e+00 4171 | 3.8188610e+00 2.5592500e+00 4172 | 3.7945620e+00 2.5791900e+00 4173 | 3.8107680e+00 2.5035180e+00 4174 | 3.7741270e+00 2.5743350e+00 4175 | 3.7742470e+00 2.5410140e+00 4176 | 3.7999340e+00 2.5763830e+00 4177 | 3.8181350e+00 2.5623210e+00 4178 | 3.7416630e+00 2.4723140e+00 4179 | 3.7776740e+00 2.5986270e+00 4180 | 3.8147400e+00 2.5522440e+00 4181 | 3.7628010e+00 2.5998750e+00 4182 | 3.7428770e+00 2.5755280e+00 4183 | 3.7738770e+00 2.6029780e+00 4184 | 3.8262330e+00 2.7001300e+00 4185 | 3.8231710e+00 2.5698660e+00 4186 | 3.7530450e+00 2.5693200e+00 4187 | 3.8219120e+00 2.5707690e+00 4188 | 3.7210680e+00 2.6085480e+00 4189 | 3.8594470e+00 2.5661270e+00 4190 | 3.8294250e+00 2.4869170e+00 4191 | 3.8688330e+00 2.6194100e+00 4192 | 3.8015390e+00 2.5523370e+00 4193 | 3.7578910e+00 2.5629880e+00 4194 | 3.7990410e+00 2.6067950e+00 4195 | 3.8436400e+00 2.6028620e+00 4196 | 3.8092760e+00 2.5550410e+00 4197 | 3.8117800e+00 2.5214570e+00 4198 | 3.8075750e+00 2.5140610e+00 4199 | 3.8381950e+00 2.6428870e+00 4200 | 3.8226570e+00 2.6179420e+00 4201 | 3.8786900e+00 2.4945930e+00 4202 | 3.8035730e+00 2.5946050e+00 4203 | 3.9313010e+00 2.5635080e+00 4204 | 3.7775200e+00 2.5455930e+00 4205 | 3.7925300e+00 2.5883040e+00 4206 | 3.7978340e+00 2.5734120e+00 4207 | 3.8234750e+00 2.5336450e+00 4208 | 3.8105630e+00 2.5607830e+00 4209 | 3.7218310e+00 2.5504480e+00 4210 | 3.8292890e+00 2.5800300e+00 4211 | 3.5819720e+00 2.6601580e+00 4212 | 3.5587410e+00 2.6922910e+00 4213 | 3.5492640e+00 2.6539130e+00 4214 | 3.5757420e+00 2.6479350e+00 4215 | 3.4950880e+00 2.6622200e+00 4216 | 3.5905480e+00 2.6501710e+00 4217 | 3.5717680e+00 2.6383040e+00 4218 | 3.5391900e+00 2.6486560e+00 4219 | 3.4851690e+00 2.7011220e+00 4220 | 3.5937320e+00 2.6006980e+00 4221 | 3.5403790e+00 2.6507360e+00 4222 | 3.5653770e+00 2.6592630e+00 4223 | 3.5500300e+00 2.6780250e+00 4224 | 3.5844660e+00 2.6112700e+00 4225 | 3.5354810e+00 2.6924100e+00 4226 | 3.5045900e+00 2.6971880e+00 4227 | 3.5687270e+00 2.6409690e+00 4228 | 3.6275750e+00 2.6496800e+00 4229 | 3.4778720e+00 2.6568510e+00 4230 | 3.6006950e+00 2.5886070e+00 4231 | 3.5467840e+00 2.6082840e+00 4232 | 3.4837890e+00 2.6221400e+00 4233 | 3.4643320e+00 2.6763700e+00 4234 | 3.4325180e+00 2.6099090e+00 4235 | 3.4967520e+00 2.5633650e+00 4236 | 3.4586070e+00 2.5817200e+00 4237 | 3.4043410e+00 2.6088510e+00 4238 | 3.5117870e+00 2.6833430e+00 4239 | 3.4617650e+00 2.6448530e+00 4240 | 3.5083910e+00 2.6330860e+00 4241 | 3.6663130e+00 2.5151000e+00 4242 | 3.5975410e+00 2.5898270e+00 4243 | 3.6559590e+00 2.5849300e+00 4244 | 3.6583090e+00 2.5727020e+00 4245 | 3.6369750e+00 2.5581670e+00 4246 | 3.6426950e+00 2.6030190e+00 4247 | 3.6633910e+00 2.6000850e+00 4248 | 3.6231300e+00 2.6137680e+00 4249 | 3.6161550e+00 2.6482090e+00 4250 | 3.6040090e+00 2.5956470e+00 4251 | 3.6546080e+00 2.5992080e+00 4252 | 3.6260180e+00 2.5155900e+00 4253 | 3.6435400e+00 2.5716150e+00 4254 | 3.5928420e+00 2.6365750e+00 4255 | 3.5999110e+00 2.5260300e+00 4256 | 3.5668710e+00 2.5570840e+00 4257 | 3.5606180e+00 2.5490370e+00 4258 | 3.5790780e+00 2.5824930e+00 4259 | 3.5760960e+00 2.5846320e+00 4260 | 3.6307250e+00 2.5270730e+00 4261 | 3.7046430e+00 2.5675650e+00 4262 | 3.6717960e+00 2.5584330e+00 4263 | 3.7010540e+00 2.4881010e+00 4264 | 3.7045640e+00 2.5666680e+00 4265 | 3.6277030e+00 2.5311440e+00 4266 | 3.6314360e+00 2.5152620e+00 4267 | 3.6391790e+00 2.5233280e+00 4268 | 3.7322590e+00 2.5594810e+00 4269 | 3.6891490e+00 2.5020530e+00 4270 | 3.6689180e+00 2.4846680e+00 4271 | 3.6430370e+00 2.5673960e+00 4272 | 3.6794630e+00 2.6181370e+00 4273 | 3.6349310e+00 2.6044860e+00 4274 | 3.6589700e+00 2.5498210e+00 4275 | 3.6858360e+00 2.5819810e+00 4276 | 3.6450160e+00 2.5241960e+00 4277 | 3.6276680e+00 2.5510250e+00 4278 | 3.6811200e+00 2.5939330e+00 4279 | 3.7138730e+00 2.5152620e+00 4280 | 3.6000610e+00 2.6455250e+00 4281 | 3.5703610e+00 2.5652420e+00 4282 | 3.6533230e+00 2.5546910e+00 4283 | 3.6411860e+00 2.5962340e+00 4284 | 3.5776520e+00 2.5387640e+00 4285 | 3.6112960e+00 2.5636990e+00 4286 | 3.5832640e+00 2.5724780e+00 4287 | 3.6075880e+00 2.5727580e+00 4288 | 3.6174730e+00 2.5796600e+00 4289 | 3.6294770e+00 2.6290740e+00 4290 | 3.6355590e+00 2.5181740e+00 4291 | 3.5503120e+00 2.5047450e+00 4292 | 3.5406920e+00 2.5124600e+00 4293 | 3.4224640e+00 2.5654880e+00 4294 | 3.5078530e+00 2.5484850e+00 4295 | 3.4866050e+00 2.5953210e+00 4296 | 3.5587560e+00 2.5795800e+00 4297 | 3.5374800e+00 2.5579820e+00 4298 | 3.5364250e+00 2.5177700e+00 4299 | 3.5843050e+00 2.5563510e+00 4300 | 3.5264060e+00 2.5715770e+00 4301 | 3.4167650e+00 2.5799260e+00 4302 | 3.5445290e+00 2.5353090e+00 4303 | 3.4720810e+00 2.5170310e+00 4304 | 3.5005460e+00 2.6249680e+00 4305 | 3.4342120e+00 2.5359550e+00 4306 | 3.5068770e+00 2.6058990e+00 4307 | 3.5834760e+00 2.5659820e+00 4308 | 3.5558570e+00 2.5984570e+00 4309 | 3.5119370e+00 2.5256330e+00 4310 | 3.4216540e+00 2.5654210e+00 4311 | 3.4500580e+00 2.6409190e+00 4312 | 3.4460350e+00 2.6757920e+00 4313 | 3.5158080e+00 2.6243180e+00 4314 | 3.5604420e+00 2.6280700e+00 4315 | 3.4752770e+00 2.5796540e+00 4316 | 3.5004100e+00 2.6175520e+00 4317 | 3.4368000e+00 2.6024680e+00 4318 | 3.4459400e+00 2.6297850e+00 4319 | 3.3579110e+00 2.5973780e+00 4320 | 3.4712270e+00 2.5850330e+00 4321 | 3.7492030e+00 2.6108860e+00 4322 | 3.7847240e+00 2.7067750e+00 4323 | 3.7369520e+00 2.6799810e+00 4324 | 3.6970080e+00 2.7275520e+00 4325 | 3.6825350e+00 2.6465830e+00 4326 | 3.7130820e+00 2.6902820e+00 4327 | 3.7204490e+00 2.6263450e+00 4328 | 3.6687080e+00 2.6729180e+00 4329 | 3.7051430e+00 2.6761550e+00 4330 | 3.7213850e+00 2.6354460e+00 4331 | 3.8082900e+00 2.6408930e+00 4332 | 3.8841170e+00 2.6606640e+00 4333 | 3.8754400e+00 2.6577160e+00 4334 | 3.9358470e+00 2.6486210e+00 4335 | 3.8323150e+00 2.6497160e+00 4336 | 3.8758000e+00 2.7042940e+00 4337 | 3.9044130e+00 2.6216760e+00 4338 | 3.8577210e+00 2.6817590e+00 4339 | 3.8918890e+00 2.6511110e+00 4340 | 3.8649830e+00 2.6551650e+00 4341 | 3.8951260e+00 2.7224840e+00 4342 | 3.8653750e+00 2.6768030e+00 4343 | 3.8955880e+00 2.6645890e+00 4344 | 3.8634570e+00 2.6449020e+00 4345 | 3.8548330e+00 2.7098600e+00 4346 | 3.9145690e+00 2.6741550e+00 4347 | 3.8699150e+00 2.7219980e+00 4348 | 3.9173920e+00 2.6681740e+00 4349 | 3.9981850e+00 2.6753820e+00 4350 | 3.8926970e+00 2.6987070e+00 4351 | 3.3355900e+00 2.8293250e+00 4352 | 3.2282780e+00 2.8805390e+00 4353 | 3.2366650e+00 2.8309010e+00 4354 | 3.3152210e+00 2.8558900e+00 4355 | 3.3053010e+00 2.8376780e+00 4356 | 3.2939420e+00 2.8012500e+00 4357 | 3.2672920e+00 2.8647460e+00 4358 | 3.2704860e+00 2.8005110e+00 4359 | 3.3073540e+00 2.8038020e+00 4360 | 3.2534810e+00 2.8505880e+00 4361 | 3.2316780e+00 2.7271460e+00 4362 | 3.2854930e+00 2.7316650e+00 4363 | 3.2060410e+00 2.8027740e+00 4364 | 3.2330820e+00 2.7578400e+00 4365 | 3.2767590e+00 2.7025270e+00 4366 | 3.2731150e+00 2.7086080e+00 4367 | 3.2895040e+00 2.7157270e+00 4368 | 3.2808870e+00 2.7135650e+00 4369 | 3.2936790e+00 2.7599780e+00 4370 | 3.3090310e+00 2.6856090e+00 4371 | 3.1169250e+00 2.6330370e+00 4372 | 3.2041810e+00 2.6346200e+00 4373 | 3.1668230e+00 2.6136370e+00 4374 | 3.1820120e+00 2.6477690e+00 4375 | 3.2030430e+00 2.6965010e+00 4376 | 3.1840010e+00 2.6604090e+00 4377 | 3.2320170e+00 2.6672430e+00 4378 | 3.2187310e+00 2.6703770e+00 4379 | 3.1949470e+00 2.6874940e+00 4380 | 3.1723050e+00 2.6696260e+00 4381 | 3.0572068e+00 2.6189330e+00 4382 | 3.1500600e+00 2.5448160e+00 4383 | 3.0616372e+00 2.6409580e+00 4384 | 3.0970880e+00 2.6318770e+00 4385 | 3.1239400e+00 2.5785010e+00 4386 | 3.1432070e+00 2.6534970e+00 4387 | 3.1114190e+00 2.5818670e+00 4388 | 3.0991621e+00 2.6265910e+00 4389 | 3.0983062e+00 2.6417580e+00 4390 | 3.1610100e+00 2.6343890e+00 4391 | 3.6038420e+00 2.8216640e+00 4392 | 3.5546150e+00 2.8532480e+00 4393 | 3.5693240e+00 2.8543260e+00 4394 | 3.6173800e+00 2.9179614e+00 4395 | 3.5049590e+00 2.8609500e+00 4396 | 3.5442860e+00 2.8724120e+00 4397 | 3.5381810e+00 2.8647980e+00 4398 | 3.5805790e+00 2.8805260e+00 4399 | 3.5693660e+00 2.8536260e+00 4400 | 3.5916720e+00 2.8512030e+00 4401 | 3.0121229e+00 3.1595580e+00 4402 | 3.0282639e+00 3.0430814e+00 4403 | 3.0626579e+00 3.1729680e+00 4404 | 3.0082592e+00 3.0591374e+00 4405 | 3.0118872e+00 3.1228630e+00 4406 | 2.9757428e+00 3.0348194e+00 4407 | 3.0482760e+00 3.1780840e+00 4408 | 3.0513952e+00 3.0724304e+00 4409 | 2.9975338e+00 3.0691219e+00 4410 | 2.9793565e+00 3.0666272e+00 4411 | 2.9676623e+00 3.1958840e+00 4412 | 2.9525262e+00 3.2130520e+00 4413 | 3.0189078e+00 3.2588530e+00 4414 | 2.9840934e+00 3.2366850e+00 4415 | 2.9883637e+00 3.1989860e+00 4416 | 2.9668662e+00 3.1906390e+00 4417 | 2.9905521e+00 3.2720150e+00 4418 | 2.9305776e+00 3.1918140e+00 4419 | 2.9521103e+00 3.1985860e+00 4420 | 2.9694582e+00 3.1936390e+00 4421 | 2.9763199e+00 3.2464030e+00 4422 | 2.9525374e+00 3.2698870e+00 4423 | 2.9563961e+00 3.2184690e+00 4424 | 2.9360216e+00 3.2182410e+00 4425 | 2.9668223e+00 3.2593680e+00 4426 | 2.9255786e+00 3.1915330e+00 4427 | 2.9382615e+00 3.2152140e+00 4428 | 2.9479209e+00 3.1960230e+00 4429 | 2.9270487e+00 3.2536150e+00 4430 | 2.9233946e+00 3.1665940e+00 4431 | 3.0081942e+00 3.3885210e+00 4432 | 2.9403103e+00 3.3967850e+00 4433 | 3.0505424e+00 3.3227340e+00 4434 | 2.9723820e+00 3.3758400e+00 4435 | 2.9838204e+00 3.4775050e+00 4436 | 2.9479126e+00 3.3486100e+00 4437 | 2.9419385e+00 3.3506040e+00 4438 | 2.9888411e+00 3.4327650e+00 4439 | 2.9886358e+00 3.4065120e+00 4440 | 2.9384604e+00 3.4129640e+00 4441 | 2.9380739e+00 3.4267590e+00 4442 | 2.9701683e+00 3.3402680e+00 4443 | 2.9934336e+00 3.4708890e+00 4444 | 3.0354348e+00 3.4070870e+00 4445 | 2.9242336e+00 3.4024090e+00 4446 | 2.9717046e+00 3.3811140e+00 4447 | 2.9518820e+00 3.4236590e+00 4448 | 2.9738200e+00 3.3775910e+00 4449 | 3.0510060e+00 3.3824020e+00 4450 | 2.9749220e+00 3.4131830e+00 4451 | 3.1909200e+00 3.4305880e+00 4452 | 3.1625530e+00 3.4780030e+00 4453 | 3.1483040e+00 3.4315430e+00 4454 | 3.1195530e+00 3.4121890e+00 4455 | 3.1580310e+00 3.4574970e+00 4456 | 3.1319340e+00 3.3691490e+00 4457 | 3.1495880e+00 3.4232330e+00 4458 | 3.1591150e+00 3.3945840e+00 4459 | 3.2264870e+00 3.3702300e+00 4460 | 3.1138270e+00 3.4653940e+00 4461 | 3.1434730e+00 3.4525860e+00 4462 | 3.1824750e+00 3.4788680e+00 4463 | 3.1372880e+00 3.3685870e+00 4464 | 3.1542840e+00 3.4769090e+00 4465 | 3.0863110e+00 3.3838860e+00 4466 | 3.1464310e+00 3.4169560e+00 4467 | 3.1343130e+00 3.4338420e+00 4468 | 3.0790643e+00 3.4547360e+00 4469 | 3.1922420e+00 3.4457810e+00 4470 | 3.0454605e+00 3.4270530e+00 4471 | 3.1711210e+00 3.1462560e+00 4472 | 3.1458590e+00 3.1793670e+00 4473 | 3.1094200e+00 3.2126500e+00 4474 | 3.1611330e+00 3.1355620e+00 4475 | 3.1840820e+00 3.1690080e+00 4476 | 3.1592360e+00 3.1383070e+00 4477 | 3.1890680e+00 3.1361050e+00 4478 | 3.1162990e+00 3.1716550e+00 4479 | 3.1604310e+00 3.1644200e+00 4480 | 3.1574870e+00 3.1649010e+00 4481 | 3.0731593e+00 3.1792010e+00 4482 | 3.1641140e+00 3.1805640e+00 4483 | 3.1257400e+00 3.1253200e+00 4484 | 3.0798250e+00 3.1406460e+00 4485 | 3.1283960e+00 3.1850260e+00 4486 | 3.2096410e+00 3.1613190e+00 4487 | 3.1504330e+00 3.1110290e+00 4488 | 3.1152630e+00 3.2200450e+00 4489 | 3.1907760e+00 3.2723710e+00 4490 | 3.1041410e+00 3.2679290e+00 4491 | -------------------------------------------------------------------------------- /Chapter09/ConcreteQualityFitting.m: -------------------------------------------------------------------------------- 1 | ConcreteData = readtable('Concrete_Data.xls'); 2 | summary(ConcreteData) 3 | X = table2array(ConcreteData(:,1:8)); 4 | Y = table2array(ConcreteData(:,9)); 5 | plotmatrix(X,Y) 6 | X = X'; 7 | Y = Y'; 8 | trainFcn = 'trainlm'; 9 | hiddenLayerSize = 10; 10 | net = fitnet(hiddenLayerSize,trainFcn); 11 | view(net) 12 | net.input.processFcns = {'removeconstantrows','mapminmax'}; 13 | net.output.processFcns = {'removeconstantrows','mapminmax'}; 14 | net.divideFcn = 'dividerand'; 15 | net.divideMode = 'sample'; 16 | net.divideParam.trainRatio = 70/100; 17 | net.divideParam.valRatio = 15/100; 18 | net.divideParam.testRatio = 15/100; 19 | net.performFcn = 'mse'; 20 | net.plotFcns = {'plotperform','plottrainstate','ploterrhist', 'plotregression', 'plotfit'}; 21 | [net,tr] = train(net,X,Y); 22 | figure, plotperform(tr) 23 | figure, plottrainstate(tr) 24 | Ytest = net(X); 25 | e = gsubtract(Y,Ytest); 26 | performance = perform(net, Y,Ytest); 27 | figure, ploterrhist(e) 28 | trOut = Ytest(tr.trainInd); 29 | vOut = Ytest(tr.valInd); 30 | tsOut = Ytest(tr.testInd); 31 | trTarg = Y(tr.trainInd); 32 | vTarg = Y(tr.valInd); 33 | tsTarg = Y(tr.testInd); 34 | plotregression(trTarg, trOut, 'Train', vTarg, vOut, 'Validation', tsTarg, tsOut, 'Testing',Y,Ytest,'All') 35 | 36 | -------------------------------------------------------------------------------- /Chapter09/Concrete_Data.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/MATLAB-for-Machine-Learning/ff92356a96f901535d866e856c0a7fec0ea512d0/Chapter09/Concrete_Data.xls -------------------------------------------------------------------------------- /Chapter09/StudentClustering.m: -------------------------------------------------------------------------------- 1 | load ClusterData.dat 2 | scatter(ClusterData(:,1),ClusterData(:,2)) 3 | C = subclust(ClusterData,0.6); 4 | size(C) 5 | hold on 6 | plot(C(:,1),C(:,2),'x','LineWidth',4,'MarkerEdgeColor','k','MarkerSize',25) 7 | [center,U,objFcn] = fcm(ClusterData,4); 8 | figure 9 | plot(objFcn) 10 | title('Objective Function Values') 11 | xlabel('Iteration Count') 12 | ylabel('Objective Function Value') 13 | maxU = max(U); 14 | index1 = find(U(1,:) == maxU); 15 | index2 = find(U(2,:) == maxU); 16 | index3 = find(U(3,:) == maxU); 17 | index4 = find(U(4,:) == maxU); 18 | figure 19 | line(ClusterData(index1,1),ClusterData(index1,2),'linestyle','none','marker','o','color','g') 20 | line(ClusterData(index2,1),ClusterData(index2,2),'linestyle','none','marker','x','color','b') 21 | line(ClusterData(index3,1),ClusterData(index3,2),'linestyle','none','marker','^','color','m') 22 | line(ClusterData(index4,1),ClusterData(index4,2),'linestyle','none','marker','*','color','r') 23 | hold on 24 | plot(center(1,1),center(1,2),'ko','markersize',15,'LineWidth',2) 25 | plot(center(2,1),center(2,2),'kx','markersize',15,'LineWidth',2) 26 | plot(center(3,1),center(3,2),'k^','markersize',15,'LineWidth',2) 27 | plot(center(4,1),center(4,2),'k*','markersize',15,'LineWidth',2) 28 | title('Student groupings') 29 | xlabel('Motivational skils') 30 | ylabel('Cognitive skills') 31 | 32 | -------------------------------------------------------------------------------- /Chapter09/ThyroidDisease.m: -------------------------------------------------------------------------------- 1 | load thyroid_dataset; 2 | InputData = thyroidInputs; 3 | TargetData= thyroidTargets; 4 | trainFcn = 'trainscg'; 5 | hiddenLayerSize = 10; 6 | net = patternnet(hiddenLayerSize, trainFcn); 7 | net.divideFcn = 'dividerand'; 8 | net.divideMode = 'sample'; 9 | net.divideParam.trainRatio = 70/100; 10 | net.divideParam.valRatio = 15/100; 11 | net.divideParam.testRatio = 15/100; 12 | net.performFcn = 'crossentropy'; 13 | net.plotFcns = {'plotperform','plottrainstate','ploterrhist', 'plotconfusion', 'plotroc'}; 14 | [net,tr] = train(net,InputData,TargetData); 15 | OutputData = net(InputData); 16 | e = gsubtract(TargetData, OutputData); 17 | performance = perform(net, TargetData, OutputData); 18 | TargetInd = vec2ind(TargetData); 19 | OutputInd = vec2ind(OutputData); 20 | percentErrors = sum(TargetInd ~= OutputInd)/numel(TargetInd); 21 | trOut = OutputData(:,tr.trainInd); 22 | vOut = OutputData (:,tr.valInd); 23 | tsOut = OutputData (:,tr.testInd); 24 | trTarg = TargetData(:,tr.trainInd); 25 | vTarg = TargetData (:,tr.valInd); 26 | tsTarg = TargetData (:,tr.testInd); 27 | figure, plotconfusion(trTarg, trOut, 'Train', vTarg, vOut, 'Validation', tsTarg, tsOut, 'Testing', TargetData,OutputData,'All') 28 | figure, plotroc(trTarg, trOut, 'Train', vTarg, vOut, 'Validation', tsTarg, tsOut, 'Testing', TargetData,OutputData,'All') 29 | 30 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Packt 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 |

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 3 | 4 | 5 | 6 | 7 | # MATLAB for Machine Learning 8 | This is the code repository for [MATLAB for Machine Learning](https://www.packtpub.com/big-data-and-business-intelligence/matlab-machine-learning?utm_source=github&utm_medium=repository&utm_campaign=9781788398435), published by [Packt](https://www.packtpub.com/?utm_source=github). It contains all the supporting project files necessary to work through the book from start to finish. 9 | ## About the Book 10 | MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. 11 | 12 | You’ll start by getting your system ready with t he MATLAB environment for machine learning and you’ll see how to easily interact with the Matlab workspace. We’ll then move on to data cleansing, mining and analyzing various data types in machine learning and you’ll see how to display data values on a plot. Next, you’ll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You’ll also explore classification techniques such as K-nearest neighbor analysis and Naive Bayes algorithm, and understand decision tree and rules learners. 13 | 14 | After this, you’ll dive into unsupervised learning and find groups of data with clustering methods such as k-means method and dendrogram. You’ll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you’ll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. 15 | 16 | At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. 17 | ##Instructions and Navigation 18 | All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02. 19 | 20 | 21 | 22 | The code will look like the following: 23 | ``` 24 | PC1 = 0.8852* Area + 0.3958 * Perimeter + 0.0043 * Compactness + 25 | 0.1286 * LengthK + 0.1110 * WidthK - 0.1195 * AsymCoef + 0.1290 * 26 | LengthKG 27 | ``` 28 | Any command-line input or output is written as follows: 29 | ``` 30 | >>10+90 31 | ans = 32 | 100 33 | ``` 34 | 35 | In this book, machine learning algorithms are implemented in the MATLAB environment. So, to reproduce the many examples in this book, you need a new version of MATLAB (R2017a recommended) and the following toolboxes: statistics and machine learning toolbox, neural network toolbox, and fuzzy logic toolbox. 36 | 37 | ## Related Products 38 | * [Visual Media Processing Using MATLAB Beginner's Guide](https://www.packtpub.com/hardware-and-creative/visual-media-processing-using-matlab-beginners-guide?utm_source=github&utm_medium=repository&utm_campaign=9781849697200) 39 | 40 | * [MATLAB Graphics and Data Visualization Cookbook](https://www.packtpub.com/big-data-and-business-intelligence/matlab-graphics-and-data-visualization-cookbook?utm_source=github&utm_medium=repository&utm_campaign=9781849693165) 41 | 42 | * [Rapid BeagleBoard Prototyping with MATLAB and Simulink](https://www.packtpub.com/hardware-and-creative/rapid-beagleboard-prototyping-matlab-and-simulink?utm_source=github&utm_medium=repository&utm_campaign=9781849696043) 43 | ### Download a free PDF 44 | 45 | If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
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