# Perceptron demo

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%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt

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def sigmoid(x):
return 1 / (1 + np.exp(-x))

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def gen_data(m):
"""Generate m random data points from each of two diferent normal
distributions with unit variance, for a total of 2*m points.

Parameters
----------
m : int
Number of points per class

Returns
-------
x, y : numpy arrays
x is a float array with shape (m, 2)
y is a binary array with shape (m,)

"""
sigma = np.eye(2)
mu = np.array([[0, 2], [0, 0]])
mvrandn = np.random.multivariate_normal
x = np.concatenate([mvrandn(mu[:, 0], sigma, m), mvrandn(mu[:, 1], sigma, m)], axis=0)
y = np.concatenate([np.zeros(m), np.ones(m)], axis=0)
idx = np.arange(2 * m)
np.random.shuffle(idx)
x = x[idx]
y = y[idx]
return x, y

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def set_limits(axis, x):
"""Set the axis limits, based on the min and max of the points.

Parameters
----------
axis : matplotlib axis object
x : array with shape (m, 2)

"""
axis.set_xlim(x[:, 0].min() - 0.5, x[:, 0].max() + 0.5)
axis.set_ylim(x[:, 1].min() - 0.5, x[:, 1].max() + 0.5)

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def init_plot(x, y, boundary, loops):
"""Initialize the plot with two subplots: one for the training
error, and one for the decision boundary. Returns a function
that can be called with new errors and boundary to update the
plot.

Parameters
----------
x : numpy array with shape (m, 2)
The input data points
y : numpy array with shape (m,)
The true labels of the data
boundary : numpy array with shape (2, 2)
Essentially, [[xmin, ymin], [xmax, ymax]]

Returns
-------
update_plot : function
This function takes two arguments, the array of errors and
the boundary, and updates the error plot with the new errors
and the boundary on the data plot.

"""
plt.close('all')
fig, (ax1, ax2) = plt.subplots(1, 2)

error_line, = ax1.plot([0], [0], 'k-')
ax1.set_xlim(0, (loops * y.size) - 1)
ax1.set_ylim(0, 15)
ax1.set_xlabel("Iteration")
ax1.set_ylabel("Training error")

colors = np.empty((y.size, 3))
colors[y == 0] = [0, 0, 1]
colors[y == 1] = [1, 0, 0]
ax2.scatter(x[:, 0], x[:, 1], c=colors, s=25)
normal_line, = ax2.plot(boundary[0, 0], boundary[0, 1], 'k-', linewidth=1.5)

set_limits(ax2, x)

plt.draw()
plt.show()

def update_plot(errors, boundary):
error_line.set_xdata(np.arange(errors.size))
error_line.set_ydata(errors)
normal_line.set_xdata(boundary[:, 0])
normal_line.set_ydata(boundary[:, 1])
set_limits(ax2, x)
plt.draw()

return update_plot

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def calc_normal(normal, weights):
"""Calculate the normal vector and decision boundary.

Parameters
----------
normal : numpy array with shape (2,)
The normal vector to the decision boundary
weights : numpy array with shape (3,)
Weights of the perceptron

Returns
-------
new_normal, boundary : numpy arrays
The new_normal array is the updated normal vector. The
boundary array is [[xmin, ymin], [xmax, ymax]] of the
boundary between the points.

"""
new_normal = normal - (np.dot(weights[:2], normal) / np.dot(weights[:2], weights[:2])) * weights[:2]
new_normal = new_normal / np.dot(new_normal, new_normal)
offset = -weights[2] * weights[:2] / np.dot(weights[:2], weights[:2])
normmult = np.array([-1000, 1000])
boundary = (new_normal[None] * normmult[:, None]) + offset[None]
return new_normal, boundary

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def demo(m=20, alpha=0.5, loops=10):
"""Run a demo of training a perceptron.

Parameters
----------
m : int
Number of datapoints per class
alpha : float
Initial learning rate
loops : int
Number of times to go through the data

"""
# generate some random data
x, y = gen_data(m)

# initialize helper variables
X = np.concatenate([x, np.ones((2 * m, 1))], axis=1)
errors = np.empty(loops * y.size)

# set up our initial weights and normal vectors
w = np.array([0, 0.2, 0])
normal, boundary = calc_normal(np.random.randn(2), w)

# initialize the plot
update_plot = init_plot(x, y, boundary, loops)

for i in range(loops):
# update the learning rate
alpha = alpha * 0.5

for j in range(y.size):
# number of iterations so far
k = i * y.size + j

# compute the output of the perceptron and the error to the true labels
output = sigmoid(np.dot(w, X.T))
errors[k] = ((y - output) ** 2).sum()

# update our weights and recalculate the normal vector
w += alpha * (y[j] - output[j]) * output[j] * (1 - output[j]) * X[j]
normal, boundary = calc_normal(normal, w)

# update the plot
update_plot(errors[:k], boundary)

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demo(loops=5)

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