def plot_in_2d(X, y=None, title=None, accuracy=None, legend_labels=None):
cmap = plt.get_cmap('viridis')
X_transformed = PCA().transform(X, 2)
x1 = X_transformed[:, 0]
x2 = X_transformed[:, 1]
class_distr = []
y = np.array(y).astype(int)
colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(y)))]
# Plot the different class distributions
for i, l in enumerate(np.unique(y)):
_x1 = x1[y == l]
_x2 = x2[y == l]
_y = y[y == l]
class_distr.append(plt.scatter(_x1, _x2, color=colors[i]))
# Plot legend
if not legend_labels is None:
plt.legend(class_distr, legend_labels, loc=1)
# Plot title
if title:
if accuracy:
perc = 100 * accuracy
plt.suptitle(title)
plt.title("Accuracy: %.1f%%" % perc, fontsize=10)
else:
plt.title(title)
# Axis labels
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.show()
@author: Eric
"""
from os import chdir
chdir("..")
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
from pca.pca import PCA
import SAILnet
imfile = "patches.mat"
imname = "patches"
nullpca = PCA()
normalpca = PCA()
whiteningpca = PCA(whiten=True)
patches = scipy.io.loadmat(imfile)[imname]
origshape = patches.shape
veclength = origshape[0] * origshape[1]
nimages = origshape[2]
# unroll images
patches = patches.reshape((veclength, nimages))
scipy.io.savemat("unrolledpatches.mat", {"patches": patches})
# the PCA object wants each row to be a data point by default
patches = np.transpose(patches)
import numpy as np
from pca.pca import PCA
def sd_normalize(mat: np.ndarray):
sd = np.sqrt(np.var(mat, 0))
return mat / sd
def eigen_normalize(mat, eigen, ep):
return mat / np.array(np.sqrt(eigen[0:2] + ep))
if __name__ == '__main__':
data = np.array([[5, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1], [0, 0, 1],
[1, 2, 1], [2, 3, 4], [0, 0, 1]])
pca = PCA(data)
out, eigen, vector = pca.fit()
pca_whitening_mat = sd_normalize(out)
pca_whitening_mat2 = eigen_normalize(out, eigen, 0.01)
print(pca_whitening_mat)
a = 1
