''' from sklearn.linear_model import LinearRegression from RegressionModel import RegressionModel from MLData import MLData from Visualization import Visualization class LLS(RegressionModel): """ This class deals with K Nearest Neighbour Classifier """ def __init__(self): self.regressor = LinearRegression() if __name__ == "__main__": data = MLData() data.loadData("../data/mobile.csv", 2, 2) #fit the classifier to the data lls = LLS() lls.fitRegressor(data) #draw line coef = lls.regressor.coef_.flatten().tolist() + [lls.regressor.intercept_] print ("LLS line coef", coef) vis = Visualization() vis.visualizeData(data, coef)
from sklearn import svm from MLData import MLData from Performance import Results from Visualization import Visualization class SVM(ClassifierModel): """ This class deals with support vector machines """ def __init__(self): self.classifier = svm.SVC(kernel="linear") if __name__ == "__main__": data = MLData() data.loadData("../data/fish.csv", 1, 2) #fit the classifier to the data svm = SVM() svm.fitClassifier(data) #find accuracy results = Results() results.calculateAccuracy(svm, data) #draw line print("svm line", svm.classifier.coef_) vis = Visualization() vis.visualizeData(data, svm.classifier.coef_)