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Simple Gradient Descent on predicting Boston Housing
SImple Gradient Descent implementations Examples
Here we are using Boston Housing Dataset which is provided by sklearn package.
If you don’t have sklearn installed, you may install via pip
pip install -U scikit-learn
Load Boston Housing DatasetPermalink
import numpy as np
from sklearn.datasets import load_boston
boston = load_boston()
print(X.shape)
# (506, 13)
Load Feature DataPermalink
boston['feature_names']
# array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
# 'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')
Load Target DataPermalink
actual = boston["target"]
print(y.shape)
# (506, )
Wrap Data with PandasPermalink
import pandas as pd
data = pd.DataFrame(X, columns=boston["feature_names"])
data["RealData"] = actual # for comparison of predictions
Apply Gradient DescentPermalink
num_epoch = 100000
num_data = X.shape[0]
learning_rate = 0.000003
w = np.random.uniform(low=-1.0, high=1.0, size=13)
b = np.random.uniform(low=-1.0, high=1.0)
for epoch in range(num_epoch):
predict = X.dot(w) + b
error = np.abs(predict - actual).mean()
# Assume that error < 5 will be enough
if error < 5:
break
if epoch % 10000 == 0:
print("{0} epoch, error={1}".format(epoch, error))
w = w - learning_rate * (predict-actual).dot(X) / num_data
b = b - learning_rate * (predict-actual).mean()
print("{0} epoch, error={1}".format(epoch, error))
ResultsPermalink
0 epoch, error=44.21782491690112
10000 epoch, error=5.465574626842506
20000 epoch, error=5.080520144919618
25569 epoch, error=4.999989852544409
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