コード例 #1
0
ファイル: pca_gen.py プロジェクト: tsaith/ufldl_tutorial
        ratio = sum_ev / total_sum
        if ratio > threshold: break
        k += 1
        
    return k   

"""
Step 0a: Load data
  Here we provide the code to load natural image data into x.
  x will be a 144 * 10000 matrix, where the kth column x(:, k) corresponds to
  the raw image data from the kth 12x12 image patch sampled.
  You do not need to change the code below.
"""

# Return patches from sample images
x = sample_images_raw('data/IMAGES_RAW.mat')

# n is the number of dimensions and m is the number of patches
n, m = x.shape
random_sel = np.random.randint(0, m, 200)
image_x = display_network(x[:, random_sel])

fig = plt.figure()
plt.imshow(image_x, cmap=plt.cm.gray)
plt.title('Raw patch images')

"""
Step 0b: Zero-mean the data (by row)
"""

x -= np.mean(x, axis=1).reshape(-1, 1)
コード例 #2
0
import sample_images
import random
import display_network
import numpy as np


##================================================================
## Step 0a: Load data
#  Here we provide the code to load natural image data into x.
#  x will be a 144 * 10000 matrix, where the kth column x(:, k) corresponds to
#  the raw image data from the kth 12x12 image patch sampled.
#  You do not need to change the code below.

patches = sample_images.sample_images_raw()
num_samples = patches.shape[1]
random_sel = random.sample(range(num_samples), 400)
display_network.display_network(patches[:, random_sel], 'raw_pca.png')

##================================================================
## Step 0b: Zero-mean the data (by row)
#  You can make use of the mean and repmat/bsxfun functions.

# patches = patches - patches.mean(axis=0)
patch_mean = patches.mean(axis=1)
patches = patches - np.tile(patch_mean, (patches.shape[1], 1)).transpose()

##================================================================
## Step 1a: Implement PCA to obtain xRot
#  Implement PCA to obtain xRot, the matrix in which the data is expressed
#  with respect to the eigenbasis of sigma, which is the matrix U.
コード例 #3
0
ファイル: pca_gen.py プロジェクト: TorosFanny/ufldl_tutorial
import sample_images
import random
import display_network
import numpy as np


##================================================================
## Step 0a: Load data
#  Here we provide the code to load natural image data into x.
#  x will be a 144 * 10000 matrix, where the kth column x(:, k) corresponds to
#  the raw image data from the kth 12x12 image patch sampled.
#  You do not need to change the code below.

patches = sample_images.sample_images_raw()
num_samples = patches.shape[1]
random_sel = random.sample(range(num_samples), 400)
display_network.display_network(patches[:, random_sel], 'raw_pca.png')

##================================================================
## Step 0b: Zero-mean the data (by row)
#  You can make use of the mean and repmat/bsxfun functions.

# patches = patches - patches.mean(axis=0)
patch_mean = patches.mean(axis=1)
patches = patches - np.tile(patch_mean, (patches.shape[1], 1)).transpose()

##================================================================
## Step 1a: Implement PCA to obtain xRot
#  Implement PCA to obtain xRot, the matrix in which the data is expressed
#  with respect to the eigenbasis of sigma, which is the matrix U.