public void InternetSampleDownload() { FrameTable table = DownloadFrameTable(new Uri("https://raw.githubusercontent.com/Dataweekends/zero_to_deep_learning_udemy/master/data/weight-height.csv")); FrameView view = table.WhereNotNull(); view.AddComputedColumn("Bmi", (FrameRow r) => { double h = (double)r["Height"]; double w = (double)r["Weight"]; return(w / (h * h)); }); FrameView males = view.Where("Gender", (string s) => (s == "Male")); FrameView females = view.Where("Gender", (string s) => (s == "Female")); SummaryStatistics maleSummary = new SummaryStatistics(males["Height"].As <double>()); SummaryStatistics femaleSummary = new SummaryStatistics(females["Height"].As <double>()); TestResult allNormal = view["Height"].As <double>().ShapiroFranciaTest(); TestResult maleNormal = males["Height"].As <double>().ShapiroFranciaTest(); TestResult femaleNormal = females["Height"].As <double>().ShapiroFranciaTest(); TestResult tTest = Univariate.StudentTTest(males["Height"].As <double>(), females["Height"].As <double>()); TestResult mwTest = Univariate.MannWhitneyTest(males["Height"].As <double>(), females["Height"].As <double>()); LinearRegressionResult result0 = males["Weight"].As <double>().LinearRegression(males["Height"].As <double>()); PolynomialRegressionResult result1 = males["Height"].As <double>().PolynomialRegression(males["Weight"].As <double>(), 1); PolynomialRegressionResult result2 = males["Height"].As <double>().PolynomialRegression(males["Weight"].As <double>(), 2); PolynomialRegressionResult result3 = males["Height"].As <double>().PolynomialRegression(males["Weight"].As <double>(), 3); //MultiLinearRegressionResult multi = view["Weight"].As<double>().MultiLinearRegression(view["Height"].As<double>(), view["Gender"].As<string>().Select(s => (s == "Male") ? 1.0 : 0.0).ToList()); }