def runMahCode(arff, shuffle=True, determ=0, training=False, lr=.1, quiet=False):
mat = Arff(arff,label_count=1)
data = mat.data[:,0:-1]
labels = mat.data[:,-1:]
PClass = PerceptronClassifier(lr=lr,shuffle=shuffle,deterministic=determ)
Accuracy = 0.0
if (training):
X_train, y_train, X_test, y_test = PerceptronClassifier.split_training(data,labels)
PClass.fit(X_train,y_train)
Accuracy = PClass.score(X_test,y_test)
else:
PClass.fit(data,labels)
Accuracy = PClass.score(data,labels)
if not quiet:
print("Accuracy = [{:.5f}]".format(Accuracy))
print("Final Weights =",PClass.get_weights())
else:
return Accuracy
# In[16]:
mat = Arff("standardVoting.arff",label_count=1)
data = mat.data[:,0:-1]
labels = mat.data[:,-1:]
test = []
train = []
iters = []
all_scores = []
print("Iterations | Training | Testing")
for i in range(5):
PClass = PerceptronClassifier(lr=.1,shuffle=True)
Accuracy = 0.0
X_train, y_train, X_test, y_test = PerceptronClassifier.split_training(data,labels)
trash, iterr, scores = PClass.fit(X_train,y_train, quiet=True)
all_scores.append(scores)
training = PClass.score(X_train,y_train)
testing = PClass.score(X_test,y_test)
print(" {:} | {:.4f} | {:.4f}".format(iterr, training, testing))
iters.append(iterr)
test.append(testing)
train.append(training)
print(f"avg iter: {np.mean(iters)}")
print(f"avg training: {np.mean(train)}")
print(f"avg test: {np.mean(test)}")
# In[17]:
