def pick_out_genres():
'''Used for picking out genres.
Shows the index and weight corresponding to the highest weighted movies
of each eigenvector.
'''
movie_matrix = movies.get_matrix_from_data()
(ls, sv, rs) = svdP(5, .01, movie_matrix)
for vec in rs:
tups = []
it = np.nditer(vec, flags=['f_index'])
while not it.finished:
tups.append((it[0], it.index))
it.iternext()
print sorted(tups, reverse=True)[:10] # Best movies in each genre
def pick_out_genres():
"""Used for picking out genres.
Shows the index and weight corresponding to the highest weighted movies
of each eigenvector.
"""
movie_matrix = movies.get_matrix_from_data()
(ls, sv, rs) = svdP(5, 0.01, movie_matrix)
for vec in rs:
tups = []
it = np.nditer(vec, flags=["f_index"])
while not it.finished:
tups.append((it[0], it.index))
it.iternext()
print sorted(tups, reverse=True)[:10] # Best movies in each genre
def analyze_movies():
'''Analyzes the accuracy of SVD with increasing k'''
movie_matrix = movies.get_matrix_from_data()
# Test reconstruction accuracy
ks = [5 + 5 * i for i in range(20)]
norms = []
for k in ks:
print k
(ls, sv, rs) = svdP(k, .01, movie_matrix)
recon = reconstruct(ls, sv, rs)
diff = abs(movie_matrix - recon)
norms.append(np.linalg.norm(diff)**2)
plt.plot(ks, norms)
plt.xlabel("Number of singular values")
plt.ylabel("Square of Frobenius Norm")
plt.show()
def analyze_movies():
"""Analyzes the accuracy of SVD with increasing k"""
movie_matrix = movies.get_matrix_from_data()
# Test reconstruction accuracy
ks = [5 + 5 * i for i in range(20)]
norms = []
for k in ks:
print k
(ls, sv, rs) = svdP(k, 0.01, movie_matrix)
recon = reconstruct(ls, sv, rs)
diff = abs(movie_matrix - recon)
norms.append(np.linalg.norm(diff) ** 2)
plt.plot(ks, norms)
plt.xlabel("Number of singular values")
plt.ylabel("Square of Frobenius Norm")
plt.show()
