def regression_line_housing_no_libs():
"""
Solution for HW1 prob 2
"""
print('Homework 1 problem 2 - No Libraries - Regression Line')
print('Housing Dataset')
test, train = utils.load_and_normalize_housing_set()
print str(len(train)) + " # in training set <--> # in test " + str(len(test))
columns = train.columns[:-1]
Y_fit = mystats.linear_regression_points(train[columns], train['MEDV'])
print 'Y_fit'
print Y_fit
#for i in range(0, len(Y_fit)):
# print str(Y_fit[i]) + ' -- ' + str(train['MEDV'][i])
row_sums = np.zeros(len(Y_fit[0]))
for col in Y_fit:
for i in range(0, len(col)):
row_sums[i] += col[i]
print row_sums
col_MSE = {}
for i, col in enumerate(columns):
col_fit = row_sums[i] # Y_fit[i] + Y_fit[-1]
col_MSE[col] = mystats.compute_MSE(col_fit, train['MEDV'])
print col_MSE
RMSE = np.sqrt(col_MSE.values())
average_MSE = utils.average(col_MSE.values())
average_RMSE = utils.average(RMSE)
print 'Average MSE: ' + str(average_MSE)
print 'Average RMSE: ' + str(average_RMSE)
def test_regression_line_housing_no_libs():
"""
Testing 2 variable solution for HW1 prob 2
"""
print('Testing linear regression with 2 columns')
test, train = utils.load_and_normalize_housing_set()
print str(len(train)) + " # in training set <--> # in test " + str(len(test))
columns = train.columns[:-1]
Y_fit = mystats.linear_regression_points(train[columns[0]], train['MEDV'])
#for i, col in enumerate(columns):
print 'Y_fit'
print Y_fit
for i in range(0, len(Y_fit)):
print str(Y_fit[i]) + ' -- ' + str(train['MEDV'][i])
print train[columns[0]]
#myplot.points([train[columns[0]], train['MEDV']])
#myplot.points([train[columns[0]], list(Y_fit[0])])
myplot.fit_v_point([train[columns[0]], train['MEDV'], list(Y_fit[0] + Y_fit[-1])])
col_MSE = {}
print columns[0]
i = 0
col = 'CRIM'
col_fit = Y_fit[i] + Y_fit[-1]
col_MSE[col] = mystats.compute_MSE_arrays(col_fit, train['MEDV'])
print col_MSE
def regression_line_spam_no_libs():
"""
Solution for HW1 prob 2
"""
print('Homework 1 problem 2 - No Libraries - Regression Line')
print('Spam Dataset')
spam_data = utils.load_and_normalize_spam_data()
test, train = utils.split_test_and_train(spam_data)
columns = train.columns[:-1]
Y_fit = mystats.linear_regression_points(train[columns], train['is_spam'])
#print 'Y_fit'
#print Y_fit
#for i in range(0, len(Y_fit)):
# print str(Y_fit[i]) + ' -- ' + str(train['is_spam'][i])
col_MSE = {}
for i, col in enumerate(columns):
col_fit = Y_fit[i] + Y_fit[-1]
col_MSE[col] = mystats.compute_MSE_arrays(col_fit, train['is_spam'])
print col_MSE
RMSE = np.sqrt(col_MSE.values())
average_MSE = utils.average(col_MSE.values())
average_RMSE = utils.average(RMSE)
print 'Average MSE: ' + str(average_MSE)
print 'Average RMSE: ' + str(average_RMSE)
