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)
