ML - Lab 4 - Perceptron ANN

import pandas as pd
import matplotlib.pyplot as plt

# Create a DataFrame
X1 = [0, 0, 1, 1]
X2 = [0, 1, 0, 1]
Y = [1, 1, -1, -1]
anndata = pd.DataFrame({"X1": X1, "X2": X2, "Y": Y})
X = anndata.loc[:, ["X1", "X2"]]
y = anndata.loc[:, "Y"]
print("DataFrame: ")
print(anndata)
print()

# Perceptron - Scatter for visualizing
plt.scatter(anndata[(anndata.Y == -1)].X1, anndata[(anndata.Y == -1)].X2, c="red", marker="+", label="-1")
plt.scatter(anndata[(anndata.Y == 1)].X1, anndata[(anndata.Y == 1)].X2, c="blue", marker="o", label="1")
plt.title("Initial points")
plt.legend()
plt.show()

# Perceptron Classifier - ANN
from sklearn.neural_network import MLPRegressor
clf = MLPRegressor(hidden_layer_sizes=(), learning_rate_init=1)
clf = clf.fit(X, y)
# Predictions
print(clf.predict([[0.1, 0.1]]))
print(clf.predict([[0.9, 0.75]]))
print(clf.predict([[0.5, 0.2]]))