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from sklearn.linear_model import LinearRegression
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from sklearn.metrics import mean_squared_error
# Fix the number of samples and our seed
# Our "true function"
def f(x):
return 1.5*x + 0.5
#Construct array of (x,f(x))-pairs where x is sampled randomly from unit interval
data = np.array([[x,f(x) ] for x in np.random.random(NUM_SAMPLES)])
# Create regular grid of x values and the values of f
gridx = np.linspace(0, 1, 200)
gridy = np.array([f(x) for x in gridx])
# Add Gaussian noise with sigma=0.6
normaly = data[:,1]+0.6*np.random.randn(NUM_SAMPLES)
#Plot the messy data
plt.scatter(data[:,0], normaly )
plt.title("Scatter plot of synthetic data with normal errors")
#Plot the true function
plt.plot(gridx, gridy, label = "True function", color = 'Red')
plt.legend(loc = 2)
# Save and clear
# Fit linear regressors to increasingly large intervals of data
lm = LinearRegression()
for i in range(1, NUM_SAMPLES+1):
# Fit the regressor[:i,0].reshape((i,1)), normaly[:i].reshape((i,1)))
# Get the predictions on all of the sample points
predictions = lm.predict(data[:,0].reshape(NUM_SAMPLES,1))
# Get MSE
mse = mean_squared_error(predictions, normaly)
# Plot the messy data
plt.scatter(data[:,0], normaly)
plt.title("Linear regression on {} points with normal error".format(i))
# Plot the true function
plt.plot(gridx, gridy, label = "True function", color = 'Red')
# Plot the regression line
plt.plot(gridx, [lm.coef_[0] * x + lm.intercept_[0] for x in gridx], label = "Linear regressor line MSE = {:0.4f}".format(mse), color = 'Green')
plt.legend(loc = 2)
# Save and clear