import numpy as np
import similar_movie
import usermodel
import corrneighbours
from sklearn.linear_model import LinearRegression
from load_ml100k import load
reviews = load()
reg = LinearRegression()
es = np.array([
usermodel.all_estimates(reviews),
corrneighbours.all_estimates(reviews),
similar_movies.all_estimates(reviews),
])
reviews = reviews.toarray()
total_error = 0.0
coefficients = []
for u in range(reviews.shape[0]):
es0 = np.delete(es, u, 1)
r0 = np.delete(reviews, u, 0)
X, Y = np.where(r0 > 0)
X = es[:, X, Y]
y = r0[r0 > 0]
reg.fit(X.T, y)
coefficients.append(reg.coef_)
r0 = reviews[u]
X = np.where(r0 > 0)
p0 = reg.predict(es[:, u, X].squeeze().T)
# published by PACKT Publishing
#
# It is made available under the MIT License
from sklearn.linear_model import LinearRegression
from load_ml100k import load
import numpy as np
import similar_movie
import usermodel
import corrneighbours
sreviews = load()
reviews = sreviews.toarray()
reg = LinearRegression()
es = np.array([
usermodel.all_estimates(sreviews),
similar_movie.all_estimates(reviews, k=1),
similar_movie.all_estimates(reviews, k=2),
similar_movie.all_estimates(reviews, k=3),
similar_movie.all_estimates(reviews, k=4),
similar_movie.all_estimates(reviews, k=5),
])
total_error = 0.0
coefficients = []
for u in xrange(reviews.shape[0]):
es0 = np.delete(es, u, 1)
r0 = np.delete(reviews, u, 0)
X, Y = np.where(r0 > 0)
X = es[:, X, Y]
y = r0[r0 > 0]
