import pandas as pa
import numpy as nu
from sklearn.linear_model import Perceptron
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler

def get_accuracy(X_train, y_train, X_test, y_test):
    perceptron = Perceptron()
    perceptron.fit(X_train, y_train)
    perceptron.transform(X_train)
    prediction = perceptron.predict(X_test)
    result = accuracy_score(y_test, prediction)
    return result

test_data = pa.read_csv("C:/Users/Roman/Downloads/perceptron-test.csv")
test_data.columns = ["class", "f1", "f2"]
train_data = pa.read_csv("C:/Users/Roman/Downloads/perceptron-train.csv")
train_data.columns = ["class", "f1", "f2"]

scaler = StandardScaler()
scaler.fit_transform(train_data[train_data.columns[1:]]).reshape(-1,1)
X_train = scaler.transform(train_data[train_data.columns[1:]])

scaler.fit_transform(train_data[train_data.columns[0]])
y_train = scaler.transform(train_data[train_data.columns[0]])

scaler.fit_transform(test_data[test_data.columns[1:]])
X_test = scaler.transform(test_data[test_data.columns[1:]])

scaler.fit_transform(test_data[test_data.columns[0]])
y_test = scaler.transform(test_data[test_data.columns[0]])




scaled_accuracy = get_accuracy(nu.ravel(X_train), nu.ravel(y_train),    nu.ravel(X_test), nu.ravel(y_test))
print(scaled_accuracy)