def run_pam(args):
set_global_seeds(args['seed'])
dataset = DataLoader(args['dataset'])
X_train, X_test, X_val, y_train, y_test, y_val = dataset.prepare_train_test_val(
args)
model = Perceptron(feature_dim=X_train.shape[-1], margin=args['margin'])
model.fit(X_train, y_train)
return model.score(X_test, y_test)
def find_best_margin(args):
""" return `best_margin / 0.1` """
set_global_seeds(args['seed'])
dataset = DataLoader(args['dataset'])
X_train, X_test, X_val, y_train, y_test, y_val = dataset.prepare_train_test_val(
args)
results = []
for margin in MARGINS:
model = Perceptron(feature_dim=X_train.shape[-1], margin=margin)
model.fit(X_train, y_train)
results.append(model.score(X_val, y_val))
return results
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from models.perceptron import Perceptron
# Use custom styling from file
matplotlib.rc_file('../plotstyle')
# Data for AND gate
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype='float32')
y = np.array([[0], [0], [0], [1]], dtype='float32')
# Define and train model
model = Perceptron(data=X, labels=y, num_input=2)
model.fit(alpha=0.1, epochs=5000)
# Print results
print('x1\tx2\tlabel\tprediction')
for i in range(X.shape[0]):
print('{x1}\t{x2}\t{label}\t{prediction}'.format(x1=X[i, 0],
x2=X[i, 1],
label=y[i, 0],
prediction=model.predict(
X[i, :])[0][0]))
# Plot results
weights = model.w
bias = model.b
x_fit, y_fit = np.linspace(-1, 2, 100), []