def main(met_train, met_test, aqi_train, aqi_test, test): while True: ch = int( input("\n\nchose among the following classifier\n" "1.Rnadom Forrest\n" "2.K-NN\n" "3.SVM\n" "4.Decision Tree\n" "5.exit\n")) if ch == 1: model, accuracy = Classifiers.Random_Forest_Classifier( met_train, met_test, aqi_train, aqi_test) print(model.predict(test)) print(accuracy) elif ch == 2: model, accuracy = Classifiers.KNN(met_train, met_test, aqi_train, aqi_test) print(model.predict(test)) print(accuracy) elif ch == 3: model, accuracy = Classifiers.SVM(met_train, met_test, aqi_train, aqi_test) print(model.predict(test)) print(accuracy) elif ch == 4: model, accuracy = Classifiers.Decision_tree( met_train, met_test, aqi_train, aqi_test) print(model.predict(test)) print(accuracy) elif ch == 5: break
def quickDemo(): trials = 5000 X, y = cl.makeData(train) index = rn.sample(range(0, 39739), trials) smallX = np.empty((trials, len(X[0]))) smally = np.empty(trials, dtype='|S30') count = 0 for i in index: smallX[count] = X[i] smally[count] = y[i] count = count + 1 start = time.time() cl.SVM(smallX, smally) print((time.time() - start) / 5) start = time.time() cl.NearestNeighbor(smallX, smally) print((time.time() - start) / 5) start = time.time() cl.MLP(smallX, smally) print((time.time() - start) / 5)
def runFullTests(): X, y = cl.makeData(train) cl.SVM(X, y) cl.NearestNeighbor(X, y) cl.MLP(X, y)
import Data import Classifiers samples = 5000 noise = 25 trainingData, trainingLabels, testData, testLabels = Data.createData( samples, noise) Classifiers.MLP(trainingData, trainingLabels, testData, testLabels) Classifiers.radialBF(trainingData, trainingLabels, testData, testLabels) Classifiers.SVM(trainingData, trainingLabels, testData, testLabels) Classifiers.RF(trainingData, trainingLabels, testData, testLabels)