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nmf update, use own svd nmf

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Kaushik Narayan R 2023-10-14 12:30:18 -07:00
parent 431f86b2a5
commit 7c0c4a1838
2 changed files with 24 additions and 39 deletions
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3
.gitignore vendored
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@ -2,4 +2,5 @@ Datasets/
Other code/
*.zip
*.env
__pycache__
__pycache__
*.json

60
Phase 2/utils.py
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@ -5,10 +5,9 @@ import random
import cv2
import numpy as np
from scipy.stats import pearsonr
from scipy.sparse.linalg import svds
from sklearn.decomposition import NMF
# from scipy.sparse.linalg import svds
# from sklearn.decomposition import NMF
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
# from sklearn.cluster import KMeans
@ -286,7 +285,7 @@ def resnet_output(image):
with torch.no_grad():
features = model(resized_image)
features = torch.nn.Softmax()(features)
return features.detach().cpu().tolist()
@ -817,17 +816,25 @@ def svd(matrix, k):
return left_singular_vectors, np.diag(singular_values), right_singular_vectors.T
def nmf(matrix, k, num_iterations=100):
def nmf(matrix, k, H=None, update_H=True, num_iterations=100):
"""
Non-negative matrix factorization by multiplicative update
Pass `H` and `update_H=False` to transform given data as per the given H matrix, else leave `H=None` and `update_H=True` to fit and transform
"""
d1, d2 = matrix.shape
# Initialize W and H matrices with random non-negative values
W = np.random.rand(d1, k)
H = np.random.rand(k, d2)
if update_H is True:
H = np.random.rand(k, d2)
for iteration in range(num_iterations):
# Update H matrix
numerator_h = np.dot(W.T, matrix)
denominator_h = np.dot(np.dot(W.T, W), H)
H *= numerator_h / denominator_h
if update_H is True:
# Update H matrix
numerator_h = np.dot(W.T, matrix)
denominator_h = np.dot(np.dot(W.T, W), H)
H *= numerator_h / denominator_h
# Update W matrix
numerator_w = np.dot(matrix, H.T)
@ -836,6 +843,7 @@ def nmf(matrix, k, num_iterations=100):
return W, H
def extract_latent_semantics_from_feature_model(
fd_collection,
k,
@ -879,9 +887,8 @@ def extract_latent_semantics_from_feature_model(
match valid_dim_reduction_methods[dim_reduction_method]:
# singular value decomposition
# sparse version of SVD to get only k singular values
case 1:
U, S, V_T = svds(feature_vectors, k=k)
U, S, V_T = svd(feature_vectors, k=k)
all_latent_semantics = {
"image-semantic": U.tolist(),
@ -906,18 +913,7 @@ def extract_latent_semantics_from_feature_model(
min_value = np.min(feature_vectors)
feature_vectors_shifted = feature_vectors - min_value
model = NMF(
n_components=k,
init="random",
solver="cd",
alpha_H=0.01,
alpha_W=0.01,
max_iter=10000,
)
model.fit(feature_vectors_shifted)
W = model.transform(feature_vectors_shifted)
H = model.components_
W, H = nmf(feature_vectors_shifted, k)
all_latent_semantics = {
"image-semantic": W.tolist(),
@ -1053,9 +1049,8 @@ def extract_latent_semantics_from_sim_matrix(
match valid_dim_reduction_methods[dim_reduction_method]:
# singular value decomposition
# sparse version of SVD to get only k singular values
case 1:
U, S, V_T = svds(feature_vectors, k=k)
U, S, V_T = svd(feature_vectors, k=k)
all_latent_semantics = {
"image-semantic": U.tolist(),
@ -1080,18 +1075,7 @@ def extract_latent_semantics_from_sim_matrix(
min_value = np.min(feature_vectors)
feature_vectors_shifted = feature_vectors - min_value
model = NMF(
n_components=k,
init="random",
solver="cd",
alpha_H=0.01,
alpha_W=0.01,
max_iter=10000,
)
model.fit(feature_vectors_shifted)
W = model.transform(feature_vectors_shifted)
H = model.components_
W, H = nmf(feature_vectors_shifted, k)
all_latent_semantics = {
"image-semantic": W.tolist(),