def getCentVec(self, contextVecs):
sample, rank, dim = contextVecs.shape
contexts = np.reshape(contextVecs, (sample * rank, dim))
pca = PCA(n_components=1)
pca.fit(contexts)
return pca.components_[0]
def compress(self, X):
n = X.shape[0]
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X,X)
D = np.sqrt(D)
#TODO:
D = self.construct_dist_graph(X , D)
# If two points are disconnected (distance is Inf)
# then set their distance to the maximum
# distance in the graph, to encourage them to be far apart.
D[np.isinf(D)] = D[~np.isinf(D)].max()
# Initialize low-dimensional representation with PCA
pca = PCA(self.k)
pca.fit(X)
Z = pca.compress(X)
# Solve for the minimizer
z,f = findMin(self._fun_obj_z, Z.flatten(), 500, D)
Z = z.reshape(n, self.k)
return Z
def compress(self, X):
n = X.shape[0]
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X, X)
D = np.sqrt(D)
sorted_indices = np.argsort(D)
G = np.zeros((n, n))
for i in range(D.shape[0]):
for j in range(self.nn + 1):
G[i, sorted_indices[i, j]] = D[i, sorted_indices[i, j]]
G[sorted_indices[i, j], i] = D[sorted_indices[i, j], i]
dist = utils.dijkstra(G)
dist[np.isinf(dist)] = dist[~np.isinf(dist)].max()
# Initialize low-dimensional representation with PCA
pca = PCA(self.k)
pca.fit(X)
Z = pca.compress(X)
# Solve for the minimizer
z, f = findMin(self._fun_obj_z, Z.flatten(), 500, dist)
Z = z.reshape(n, self.k)
return Z
def compress(self, X):
n = X.shape[0]
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X, X)
D = np.sqrt(D)
# D is symmetric matrix
geoD = np.zeros((n, n))
# find nn-neighbours
for i in range(n):
sort = np.argsort(D[:, i])
neigh = np.setdiff1d(sort[0:self.nn + 1], i)
# find the nn+1 smallest indexes that are not i
for j in range(len(neigh)):
t = neigh[j]
geoD[i, t] = D[i, t]
geoD[t, i] = D[t, i]
D = utils.dijkstra(geoD)
# for disconnected vertices (distance is Inf)
# set their dist = max_dist(graph)
# to encourage they are far away from each other
D[np.isinf(D)] = D[~np.isinf(D)].max()
# Initialize low-dimensional representation with PCA
pca = PCA(self.k)
pca.fit(X)
Z = pca.compress(X)
# Solve for the minimizer
z, f = findMin(self._fun_obj_z, Z.flatten(), 500, D)
Z = z.reshape(n, self.k)
return Z
def getCxtSubspace(wl, dim, var_threshold=0.45):
emb = []
for word in wl:
if (word not in vecDict):
print "non-exist:", word
continue
wordEmbed = dict[word]
emb.append(wordEmbed)
emb = np.array(emb)
pca = PCA()
pca.fit(emb)
varList = pca.explained_variance_ratio_
cand = 0
varSum = 0
for var in varList:
varSum += var
cand += 1
if (varSum >= var_threshold):
break
pca = PCA(n_components=cand)
pca.fit(emb)
top_embed = pca.components_
print "dim:", len(top_embed.tolist()), cand
return top_embed.tolist()
def compress(self, X):
n = X.shape[0]
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X, X)
D = np.sqrt(D)
# Construct nearest neighbour graph
G = np.zeros([n, n])
for i in range(n):
neighbours = np.argsort(D[i])[:self.nn + 1]
for j in neighbours:
G[i, j] = D[i, j]
G[j, i] = D[j, i]
# Compute ISOMAP distances
D = utils.dijkstra(G)
# If two points are disconnected (distance is Inf)
# then set their distance to the maximum
# distance in the graph, to encourage them to be far apart.
D[np.isinf(D)] = D[~np.isinf(D)].max()
# Initialize low-dimensional representation with PCA
pca = PCA(self.k)
pca.fit(X)
Z = pca.compress(X)
# Solve for the minimizer
z, f = findMin(self._fun_obj_z, Z.flatten(), 500, D)
Z = z.reshape(n, self.k)
return Z
def test_pca(self):
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
pca = PCA(n_comp=2)
pca.fit(X)
self.assertEqual(
np.allclose(pca.explained_variance,
np.array([0.9924, 0.0075]),
atol=1e-3), True)
def cross_validation(X, Y, folds=5, split_value=0.3, name="lda"):
# Y = Y.reshape((len(Y), 1))
# X = np.hstack((X, Y))
# part = -1
#
# if split:
# part = split_value
# else:
# part = int(np.math.ceil(len(X) / folds))
# scores = []
#
# for i in range(folds):
# test = np.array(X[i * part: (i + 1) * part])
# test = [list(d) for d in test]
# train = [np.array(j) for j in X if list(j) not in test]
# test = np.array(test)
# train = np.array(train)
#
# train_x, train_y = train[:, :-1], train[:, -1]
# test_x, test_y = test[:, :-1], test[:, -1]
#
# print(train_x.shape)
# print(test_x.shape)
scores = []
for fold in range(folds):
train_x, test_x, train_y, test_y = train_test_split(
X, Y, shuffle=True, test_size=split_value)
if name == "lda":
lda = LDA()
lda.fit(train_x, train_y)
lda_train_x = lda.transform(train_x)
lda_test_x = lda.transform(test_x)
else:
pca = PCA()
pca.fit(train_x)
pca_train_x = pca.transform(train_x)
pca_test_x = pca.transform(test_x)
'''classifier'''
lr = LogisticRegression(solver='saga', n_jobs=4)
lr.fit(train_x, train_y)
score = lr.score(test_x, test_y)
scores.append(score)
print("accuracy on fold ", fold, " : ", score)
mean = np.mean(scores)
std = np.std(scores)
print("mean accuracy : ", mean)
print("standard deviation : ", std)
return mean, std, scores
def pcaSenEmb(sent_vecs, var_threshold=0.6):
"""
output: basis of context space
"""
pca = PCA()
pca.fit(sent_vecs)
var_list = pca.explained_variance_ratio_
cand = 0
var_sum = 0
for var in var_list:
var_sum += var
cand += 1
if (var_sum >= var_threshold):
break
basis = pca.components_
return basis
def compress(self, X):
n = X.shape[0]
k = self.k
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X, X)
D = np.sqrt(D)
# Initialize low-dimensional representation with PCA
pca = PCA(k)
pca.fit(X)
Z = pca.compress(X)
# Solve for the minimizer
z, f = findMin(self._fun_obj_z, Z.flatten(), 500, D)
Z = z.reshape(n, k)
return Z
def pcaContexts(self, idxList, idx=-1, contextMatrix=None):
'''
input: context indices
output: pca vectors
'''
vecs = self.vecMatrix[np.array(idxList)]
# randIdx = np.random.randint(0, self.vocabSize, size=(1,), dtype='i')
# vecs = self.vecMatrixNorm[randIdx]
pca = PCA(n_components=self.pcaRank)
pca.fit(vecs)
contextVecs = pca.components_[0:self.pcaRank]
if idx >= 0:
contextMatrix[idx] = contextVecs
del vecs
return contextVecs, sum(pca.explained_variance_ratio_)
def pca_subspace(elements, embedding_matrix, vector_dim, mean_centering,
numComponents, debugInfo):
ferr = open("errors_pca_representation", "a+")
flog = open("logs_pca_representation", "a+")
if embedding_matrix.ndim == 1: # only one word in the sentence, do nothing (no PCA), the vector-space of the word itself is the subspace
ferr.write("[No PCA]: Only a single element from " +
" ".join(elements) +
" found in supplied embeddings for the document" +
"_".join(debugInfo) + "\n")
subspace = embedding_matrix
singularValues = np.array([1.0])
energyRetained = 1.0
else:
flog.write("Original NumComponents: " + str(numComponents) +
" NumElements: " + str(embedding_matrix.shape[0]) + "\t")
numComponents = min(embedding_matrix.shape[0],
embedding_matrix.shape[1], numComponents)
flog.write("New NumComponents: " + str(numComponents) + "\n")
pca = PCA(n_components=numComponents, mean_centering=mean_centering)
try:
pca.fit(embedding_matrix)
subspace = pca.components_
if numComponents == 1: # convert matrix to vector when numComponents = 1
subspace = subspace.T.reshape(-1)
energyRetained = np.sum(pca.explained_variance_ratio_)
singularValues = pca.singular_values_
except (
np.linalg.LinAlgError, ZeroDivisionError
) as e: # Fails (svd doesn't converge) for some reason. Use the word-vector average in this case!
ferr.write("[SVD Error]: No subspace constructed for " +
" ".join(elements) + " in the document: " +
"_".join(debugInfo) + "\n")
subspace = np.mean(embedding_matrix, axis=0)
singularValues = np.array([1.0])
energyRetained = 1.0
ferr.close()
flog.close()
return subspace, singularValues, energyRetained
def compress(self, X):
n = X.shape[0]
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X, X)
D = np.sqrt(D)
np.fill_diagonal(D, np.inf)
########
#"finding the neighbor at each point"
G = np.matrix(np.ones((n, n)) * 0)
for i in range(n):
neighbours = np.argsort(D[:, i])
#want only the k nearest
for j in neighbours[1:self.nn + 1]:
G[i, j] = D[i, j]
G[j, i] = D[j, i]
#weighted shortest path between points (dijksta's)
D = np.zeros((n, n))
for i in range(n):
for j in range(i + 1, n):
D[i, j] = utils.dijkstra(G, i, j)
########
# If two points are disconnected (distance is Inf)
# then set their distance to the maximum
# distance in the graph, to encourage them to be far apart.
D[np.isinf(D)] = D[~np.isinf(D)].max()
# Initialize low-dimensional representation with PCA
pca = PCA(self.k)
pca.fit(X)
Z = pca.compress(X)
# Solve for the minimizer
z, f = findMin(self._fun_obj_z, Z.flatten(), 500, D)
Z = z.reshape(n, self.k)
return Z
def compress(self, X):
n = X.shape[0]
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X, X)
D = np.sqrt(D)
########
# TODO #
G = np.full((n, n), np.inf)
for i in range(n):
for j in range(n):
#temp = np.list(D[i]).sort
temp = sorted(D[i])
#print(self.nn+1)
if D[i][j] in temp[:(self.nn + 1)]:
G[i][j] = D[i][j]
for i in range(n):
for j in range(n):
D[i][j] = utils.dijkstra(G, i, j)
########
# If two points are disconnected (distance is Inf)
# then set their distance to the maximum
# distance in the graph, to encourage them to be far apart.
D[np.isinf(D)] = D[~np.isinf(D)].max()
#G[np.isinf(G)] = G[~np.isinf(G)].max()
# Initialize low-dimensional representation with PCA
pca = PCA(self.k)
pca.fit(X)
Z = pca.compress(X)
# Solve for the minimizer
z, f = findMin(self._fun_obj_z, Z.flatten(), 500, D)
Z = z.reshape(n, self.k)
return Z
def Bonus2():
'''
Visualization of the first 10 eigen vectors.
'''
# raw = genfromtxt('digits-raw.csv', delimiter=',')
raw = genfromtxt('digits-raw-small.csv', delimiter=',')
X = raw[:, 2:]
pca = PCA(10)
eigvec = pca.fit(X)
eigimg = eigvec.reshape(10, 28, 28)
for r in range(2):
for c in range(5):
i = r * 5 + c
subplot(2, 5, i + 1)
imshow(eigimg[i], cmap='gray')
title(str(i))
show()
from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np
from pca import PCA
#data = datasets.load_digits()
data = datasets.load_iris()
X = data.data
y = data.target
# Project the data onto the 2 primary principal components
pca = PCA(2)
pca.fit(X)
X_projected = pca.transform(X)
print('Shape of X:', X.shape)
print('Shape of transformed X:', X_projected.shape)
x1 = X_projected[:, 0]
x2 = X_projected[:, 1]
plt.scatter(x1,
x2,
c=y,
edgecolor='none',
alpha=0.8,
cmap=plt.cm.get_cmap('viridis', 3))
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.colorbar()
if __name__ == '__main__':
struct_log = "./data/HDFS/HDFS_100k.log_structured.csv"
## 1. 加载日志文件 提取特征向量
x_train, _ = load_HDFS(struct_log)
feature_extractor = FeatureExtractor()
x_train = feature_extractor.fit_transform(x_train,
term_weighting='tf-idf',
normalization='zero-mean')
## 2. Train an unsupervised model
print('Train phase:')
# Initialize PCA, or other unsupervised models, LogClustering, InvariantsMiner
model = PCA()
# Model hyper-parameters may be sensitive to log data, here we use the default for demo
model.fit(x_train)
# Make predictions and manually check for correctness. Details may need to go into the raw logs
y_train = model.predict(x_train)
print(f"y_train: {y_train}")
## 3. Use the trained model for online anomaly detection
print('Test phase:')
# Load another new log file. Here we use struct_log for demo only
x_test, _ = load_HDFS(struct_log)
# Go through the same feature extraction process with training, using transform() instead
x_test = feature_extractor.transform(x_test)
# Finally make predictions and alter on anomaly cases
y_test = model.predict(x_test)
print(f"y_test: {y_test}")
coloredlogs.install(level='DEBUG', logger=logger)
else:
coloredlogs.install(level='WARNING', logger=logger)
logger.info('Fetching data...')
data = fetch_data(ratio=0.8)
X_train, y_train = data['train']
D, N = X_train.shape
pca = PCA(n_comps=M, standard=standard, logger=logger)
logger.info('Applying PCA with M=%d' % M)
# normalise data
W_train = pca.fit(X_train)
logger.debug('W_train.shape=%s' % (W_train.shape,))
X_test, y_test = data['test']
I, K = X_test.shape
assert I == D, logger.error(
'Number of features of test and train data do not match, %d != %d' % (D, I))
W_test = pca.transform(X_test)
logger.debug('W_test.shape=%s' % (W_test.shape,))
classes = set(y_train.ravel())
C = len(classes)
combs = list(itertools.combinations(classes, 2))
# -*- coding: utf-8 -*- """ Created on Fri Feb 7 23:53:40 2020 @author: ABOLI """ from pca import PCA X = [] X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) pca_model = PCA(2) pca_model.fit(X) print(pca_model.variance_ratio) print(pca_model.transform(X))
data = raw_data[[
'Gender', 'Married', 'Education', 'ApplicantIncome', 'LoanAmount',
'Credit_History'
]]
x = data.to_numpy()
# x = ss.fit_transform(x)
print("[INFO] Standardizing input vectors ... ")
for i in range(x.shape[0]):
# standardize each vector
x[i] = standardize(x[i])
print("[INFO] Implementing principal components analysis ... ")
pca = PCA()
x = pca.fit(x)
y = raw_data['outcome'].to_numpy()
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
model = KernelSVM()
model.fit(x_train, y_train, alpha=0.01, iterations=100)
predictions = model.predict(x_test)
accuracy = accuracy_score(predictions, y_test)
print("[INFO] Home made recipe : " + str(accuracy))
model = SVC(kernel='rbf')
model.fit(x_train, y_train)
y = data.target
# Minimum - maximum normalizasyon işlemi
X_min_max = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
fig, axes = plt.subplots(1, 2)
axes[0].scatter(X[:, 0], X[:, 1], c=y)
axes[0].set_title("Gerçek Veri")
axes[1].scatter(X_min_max[:, 0], X_min_max[:, 1], c=y)
axes[1].set_title("Min-Max Norm. Veri")
plt.show()
# Verileri iki temel bileşen ile gösterme
from pca import PCA
pca = PCA(2)
pca.fit(X_min_max)
X_projected = pca.transform(X_min_max)
print('Min-Max Normalizasyonlu X:', X_min_max.shape) # (150, 4)
print('PCA Uygulanan X:', X_projected.shape) # (150, 2)
x1 = X_projected[:, 0]
x2 = X_projected[:, 1]
plt.scatter(x1,
x2,
c=y,
edgecolor='none',
alpha=0.8,
cmap=plt.cm.get_cmap('viridis', 3))
#
# bar.finish()
# end_time = time.time()
# print("Accuracy for Simple Nearest Neighbour @rank 1 : ", "{:.4%}".format(rank_one_score / len(query_labels)))
# print("Accuracy for Simple Nearest Neighbour @rank 5 : ", "{:.4%}".format(rank_five_score / len(query_labels)))
# print("Accuracy for Simple Nearest Neighbour @rank 10 : ", "{:.4%}".format(rank_ten_score / len(query_labels)))
#
# print("Computation Time: %s seconds" % (end_time - start_time))
# PCA-MMC
print("-----PCA_MMC-----")
pca = PCA(original_train_features,
original_train_labels,
M=500,
low_dimension=False)
pca.fit()
mmc = MMC_Supervised(max_iter=20, convergence_threshold=1e-5)
mmc_metric = mmc.fit(pca.train_sample_projection, original_train_labels)
transformed_features = mmc_metric.transform(features)
transformed_query_features = transformed_features[query_idxs - 1]
n = 10
start_time = time.time()
rank_one_score = 0
rank_five_score = 0
rank_ten_score = 0
bar.start()
for k in range(len(query_features)):
bar.update(k + 1)
feature_vector = transformed_query_features[k]
gallery_vectors = transformed_features[gallery_data_idx[k] - 1]
import numpy as np
from pca import PCA, PcaType
data = np.array([[2.5, 0.5, 2.2, 1.9, 3.1, 2.3, 2.0, 1.0, 1.5, 1.1],
[2.4, 0.7, 2.9, 2.2, 3.0, 2.7, 1.6, 1.1, 1.6, 0.9]])
pca = PCA()
pca.fit(data=data, pcaType=PcaType.Two)
pca.explained_variance_ratio()
pca.get_covariance()
pca.singular_values()
pca.transform(data=data, n_components=2)
import numpy
from pca import PCA
# we'll create a random dataset of 5 variables and 100 samples
random_dataset = numpy.random.rand(100, 5)
# define a pca object and specify a number of components
pca_ = PCA(n_components=2)
# fit the model using dataset
pca_.fit(dataset=random_dataset)
# transform the dataset
new_dataset = pca_.transform(dataset=random_dataset)
# print the new and old data shapes
print("Original shape:{}, new shape: {}".format(random_dataset.shape,
new_dataset.shape))
train_y = np.asarray(train_y)
test_y = np.asarray(test_y)
print(test_y.shape)
print(np.unique(Y))
type = int(input("pca or lda or both ?"))
if type == 1:
if d == 2:
split = float(1 / 6)
else:
split = 0.3
s = "pca"
pca = PCA()
pca.fit(train_x)
joblib.dump(pca.eigen_vectors, "pca_prjection_" + str(d) + ".pkl")
pca_train_x = pca.transform(train_x)
pca_test_x = pca.transform(test_x)
# del pca
print("pca done")
lr = LogisticRegression(solver='saga', n_jobs=4)
lr.fit(pca_train_x, train_y)
print("accuracy on test data : ", lr.score(pca_test_x, test_y))
pr = lr.predict_proba(pca_test_x)
pt = lr.predict(pca_test_x)
tt = [np.argmax(i) for i in pr[:10]]
print(tt)
def train(settings):
Xtrain, ytrain, Xval, yval, Xtest, ytest = cross_val(
path=settings[PATH], k=settings[FOLDS], emotions=settings[EMOTIONS])
# Each fold will have a new model that is used on the test data one time. The results are stored here
test_loss, test_acc = [], []
# Save all the models, this way we can access their loss and accuracy stats
models = []
# List of confusion matrix for later task
cms = []
# For every fold, fit a new PCA, train new model, do validation, save the best model
for k in range(settings[FOLDS]):
Xtrain_k, ytrain_k, Xval_k, yval_k, Xtest_k, ytest_k = Xtrain[
k], ytrain[k], Xval[k], yval[k], Xtest[k], ytest[k]
# Shuffle so there is no pattern
Xtrain_k, ytrain_k = shuffle_data(Xtrain_k, ytrain_k)
Xval_k, yval_k = shuffle_data(Xval_k, yval_k)
Xtest_k, ytest_k = shuffle_data(Xtest_k, ytest_k)
logging.info("Started fold number: {}".format(k))
# Convert to numpy arrays
Xtrain_k, Xval_k, Xtest_k = np.array(Xtrain_k), np.array(
Xval_k), np.array(Xtest_k)
# Based on model there is different functions that needs to be set
if settings[MODEL] == SoftmaxRegression:
ytrain_k, yval_k, ytest_k = one_hot_encode(
ytrain_k), one_hot_encode(yval_k), one_hot_encode(ytest_k)
loss_function = softmax_loss_function
accuracy = softmax_accuracy
else:
ytrain_k, yval_k, ytest_k = np.reshape(
ytrain_k,
(-1, 1)), np.reshape(yval_k,
(-1, 1)), np.reshape(ytest_k, (-1, 1))
loss_function = logistic_loss_function
accuracy = logistic_accuracy
# Fit the pca only using training data
pca = PCA(settings[NUM_COMPONENTS])
pca.fit(Xtrain_k)
# Project Xtrain, Xval, Xtest onto the principal components
Xtrain_k, Xval_k, Xtest_k = pca.transform(Xtrain_k), pca.transform(
Xval_k), pca.transform(Xtest_k)
# Make new model for this fold
model = settings[MODEL](settings)
best_weights, min_loss = model.weights, np.inf
for epoch in range(1, settings[EPOCHS] + 1):
# Select method for updating weights
if settings[BATCH]:
model.batch_gradient_descent(Xtrain_k, ytrain_k)
else:
model.stochastic_gradient_descent(Xtrain_k, ytrain_k)
# Using an objective function to calculate the loss, and calculate the accuracy
train_loss = loss_function(model, Xtrain_k, ytrain_k)
val_loss = loss_function(model, Xval_k, yval_k)
train_acc = accuracy(model, Xtrain_k, ytrain_k)
val_acc = accuracy(model, Xval_k, yval_k)
# Save the result for later graphs
model.train_loss.append(train_loss)
model.val_loss.append(val_loss)
model.train_acc.append(train_acc)
model.val_acc.append(val_acc)
# Check if this is the lowest loss so far, if then save weights for best model
if val_loss < min_loss:
best_weights = np.copy(model.weights)
min_loss = val_loss
# Status update on how the training goes
if epoch % 10 == 0:
logging.info(
"Epoch: {}, Train_loss: {} , Val_loss: {}, Train_acc: {}, Val_acc: {}"
.format(epoch, train_loss, val_loss, train_acc, val_acc))
# Now update the weights in the model to the best weights
model.weights = best_weights
# Use this model on the test data, and save loss & accuracy
test_loss.append(loss_function(model, Xtest_k, ytest_k))
test_acc.append(accuracy(model, Xtest_k, ytest_k))
if settings[MODEL] == SoftmaxRegression:
cf_matrix = confusion_matrix(model, Xtest_k, ytest_k)
cms.append(cf_matrix)
# Model finished, add it to list of models
models.append(model)
# Calculate the average test_loss and test_acc
avg_test_loss, avg_test_acc = np.mean(test_loss), np.mean(test_acc)
std_test_acc = np.std(test_acc)
logging.info("Average Test Loss Overall Folds: {}".format(avg_test_loss))
logging.info(
"Average Test Accuracy Overall Folds: {}".format(avg_test_acc))
logging.info("Std Test Accuracy Overall Folds: {}".format(std_test_acc))
logging.info("Generating plots")
train_losses = [model.train_loss for model in models]
val_losses = [model.val_loss for model in models]
train_acces = [model.train_acc for model in models]
val_acces = [model.val_acc for model in models]
graph_loss(train_losses, val_losses, settings)
graph_acc(train_acces, val_acces, settings)
pca.display_pc(settings)
# Visualize the cf matrix and weights for each emotion
if settings[MODEL] == SoftmaxRegression:
avg_cf_matrix = np.mean(cms,
axis=0) # Take the average of all matrixes
graph_cm(avg_cf_matrix, settings)
visualize_weights(models, pca, settings)
return train_losses
plt.ylabel("$x_{%d}$" % f2)
for i in range(n):
plt.annotate(animals[i], (X[i, f1], X[i, f2]))
utils.savefig('two_random_features.png')
elif question == '2.2':
dataset = load_dataset('animals.pkl')
X = dataset['X'].astype(float)
animals = dataset['animals']
n, d = X.shape
# standardize columns
X = utils.standardize_cols(X)
model = PCA(k=2)
model.fit(X)
Z = model.compress(X)
fig, ax = plt.subplots()
plt.ylabel('z2')
plt.xlabel('z1')
ax.scatter(Z[:, 0], Z[:, 1])
for i in range(n):
ax.annotate(animals[i], (Z[i, 0], Z[i, 1]))
utils.savefig('q2_2_PCA_animals.png')
elif question == '3.1':
X = load_dataset('highway.pkl')['X'].astype(float) / 255
n, d = X.shape
print(n, d)
h, w = 64, 64 # height and width of each image
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA as sklearn_pca_model
iris_dataset = datasets.load_iris()
orginal_data = iris_dataset.data
target = iris_dataset.target
#print(orginal_data[:20])
#print(target[:20])
#print(orginal_data.shape)
#print(target.shape)
print("PCA FROM SCRATCH:")
pca_from_scratch = PCA(n_components=2)
pca_from_scratch.fit(orginal_data)
transformed_data = pca_from_scratch.transform(orginal_data)
#transformed_data = pca_from_scratch.fit_transform(orginal_data)
pca_from_scratch.plot_cov_matrix()
pca_from_scratch.plot_cumulative_explained_variance_ratio()
print(pca_from_scratch.components)
print(pca_from_scratch.explained_variance())
print(pca_from_scratch.explained_variance_ratio())
print()
print("PCA SCIKIT-LEARN:")
sklearn_pca = sklearn_pca_model(n_components=2)
scaler = StandardScaler()
scaler.fit(orginal_data)
