Page 154 - Data Science Algorithms in a Week

Regression

# This function will apply gradient descent algorithm
# to learn the linear model.
def learn_linear_parameter(data, learning_rate,
acceptable_error, LIMIT):
parameter = [1.0, 1.0]
old_parameter = [1.0, 1.0]
for i in range(0, LIMIT):
gradient = linear_gradient(data, old_parameter)
# Update the parameter with the Least Mean Squares rule.
parameter[0] = old_parameter[0] + learning_rate * gradient[0]
parameter[1] = old_parameter[1] + learning_rate * gradient[1]
# Calculate the error between the two parameters to compare with
# the permissible error in order to determine if the calculation
# is suffiently accurate.
if abs(parameter[0] - old_parameter[0]) <= acceptable_error
and abs(parameter[1] - old_parameter[1]) <= acceptable_error:
return parameter
old_parameter[0] = parameter[0]
old_parameter[1] = parameter[1]
return parameter
# Calculate the y coordinate based on the linear model predicted.
def predict_unknown(data, linear_parameter):
for (x, y) in data:
print(x, linear_parameter[0] + linear_parameter[1] * float(x))

# Program start
csv_file_name = sys.argv[1]
# The maximum number of the iterations in the batch learning algorithm.
LIMIT = 100
# Suitable parameters chosen for the problem given.
learning_rate = 0.0000001
acceptable_error = 0.001

(heading, complete_data, incomplete_data,
enquired_column) = common.csv_file_to_ordered_data(csv_file_name)
linear_parameter = learn_linear_parameter(
complete_data, learning_rate, acceptable_error, LIMIT)
print("Linear model:\n(p0,p1)=" + str(linear_parameter) + "\n")
print("Unknowns based on the linear model:")
predict_unknown(incomplete_data, linear_parameter)

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