PYTHON need help with defining a perceptron algorithm to fit the two methods at the bottom:

dataset = pd.read_csv('mushrooms.csv') # can get from https://www.kaggle.com/mig555/mushroom-classification

label_encoder = LabelEncoder()

for column in dataset.columns:

dataset[column] = label_encoder.fit_transform(dataset[column])

labels = dataset['class']

T = dataset.drop('class', axis = 1)

X = dataset

from sklearn import preprocessing scaler = preprocessing.StandardScaler()

normalized_X = scaler.fit_transform(X)

normalized_X = pd.DataFrame(normalized_X, dataset)

X = normalized_X

ones = np.ones([X.shape[0], 1])

X = np.concatenate((ones, X), axis = 1)

""" Perceptron Algorithm """

maxiter = 1000

alpha = 0.1

w = np.zeros(X.shape[1])

# YOUR CODE...

def train():

#YOUR CODE

def predict(X, w):

""" Predicting the label for the input data """ #YOUR CODE

return np.where(np.dot(X, w) >= 0.0, 1, 0)