In [1]:

    

import pandas as pd
import os

from scipy import misc
from mpl_toolkits.mplot3d import Axes3D
import matplotlib
import matplotlib.pyplot as plt

# Look pretty...
# matplotlib.style.use('ggplot')
plt.style.use('ggplot')


    

In [2]:

    

# TODO: Start by creating a regular old, plain, "vanilla"
# python list. You can call it 'samples'.
samples = []

# TODO: Write a for-loop that iterates over the images in the
# Module4/Datasets/ALOI/32/ folder, appending each of them to
# your list. Each .PNG image should first be loaded into a
# temporary NDArray, just as shown in the Feature
# Representation reading.
#
# Optional: Resample the image down by a factor of two if you
# have a slower computer. You can also convert the image from
# 0-255  to  0.0-1.0  if you'd like, but that will have no
# effect on the algorithm's results.
#

for file in os.listdir('Datasets/ALOI/32'):
    a = os.path.join('Datasets/ALOI/32', file)
    img = misc.imread(a)#.reshape(-1)
    samples.append(img)
print(type(samples))
print(len(samples))

for file1 in os.listdir('Datasets/ALOI/32i'):	# Also append the 32i images to the list/dataframe
    b = os.path.join('Datasets/ALOI/32i', file1)
    img1 = misc.imread(b).reshape(-1)
    samples.append(img1)

print(type(samples))
print(len(samples))

colors = []
for i in range(72):
	colors.append('b')
for j in range(12):
	colors.append('r')


    

    




<class 'list'>
72
<class 'list'>
84

In [7]:

    

df = pd.DataFrame(samples) # Convert list of numpy arrays to Pandas DataFrame
df


    

    
Out[7]:






  

    

      

      
0
    
  
  

    

      
0
      
[[4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
    
    

      
1
      
[[4, 4, 4, 3, 4, 4, 3, 3, 4, 4, 3, 3, 3, 4, 4,...
    
    

      
2
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
3
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
4
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
5
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
6
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
7
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
8
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
9
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
10
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
11
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
12
      
[[4, 4, 4, 4, 4, 4, 3, 3, 4, 4, 3, 4, 4, 4, 4,...
    
    

      
13
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
14
      
[[4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 4,...
    
    

      
15
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
16
      
[[4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4,...
    
    

      
17
      
[[5, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
18
      
[[5, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
19
      
[[4, 4, 4, 5, 5, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4,...
    
    

      
20
      
[[4, 4, 4, 4, 4, 4, 4, 5, 4, 5, 5, 5, 5, 4, 5,...
    
    

      
21
      
[[4, 4, 4, 5, 5, 4, 5, 5, 5, 4, 4, 4, 5, 5, 4,...
    
    

      
22
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
23
      
[[4, 4, 4, 5, 4, 4, 4, 4, 5, 5, 4, 4, 4, 5, 4,...
    
    

      
24
      
[[4, 4, 4, 4, 4, 4, 5, 4, 5, 5, 5, 5, 4, 5, 4,...
    
    

      
25
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
26
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5,...
    
    

      
27
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
28
      
[[4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 5, 5, 5, 5, 5,...
    
    

      
29
      
[[3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
...
      
...
    
    

      
54
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 4, 4,...
    
    

      
55
      
[[5, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
56
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
57
      
[[4, 4, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
58
      
[[4, 4, 4, 4, 3, 4, 4, 3, 4, 4, 3, 4, 3, 4, 4,...
    
    

      
59
      
[[4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 5, 4,...
    
    

      
60
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
61
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
62
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
63
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
64
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
65
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
66
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
67
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
68
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
69
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
70
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
71
      
[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
    
    

      
72
      
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
    
    

      
73
      
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
    
    

      
74
      
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
    
    

      
75
      
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
    
    

      
76
      
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
    
    

      
77
      
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
    
    

      
78
      
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
    
    

      
79
      
[4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 4, 5, 4, 4, 5, ...
    
    

      
80
      
[4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 5, 5, ...
    
    

      
81
      
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
    
    

      
82
      
[5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
    
    

      
83
      
[4, 4, 4, 3, 4, 4, 3, 3, 4, 4, 4, 4, 4, 4, 3, ...
    
  

84 rows × 1 columns

In [5]:

    

# Run Isomap on the DataFrame:
from sklearn import manifold
iso = manifold.Isomap(n_neighbors = 6, n_components = 3)
iso.fit(df)


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-41515920dac5> in <module>()
      2 from sklearn import manifold
      3 iso = manifold.Isomap(n_neighbors = 6, n_components = 3)
----> 4 iso.fit(df)

D:\anaconda\lib\site-packages\sklearn\manifold\isomap.py in fit(self, X, y)
    162         self : returns an instance of self.
    163         """
--> 164         self._fit_transform(X)
    165         return self
    166 

D:\anaconda\lib\site-packages\sklearn\manifold\isomap.py in _fit_transform(self, X)
    101 
    102     def _fit_transform(self, X):
--> 103         X = check_array(X)
    104         self.nbrs_ = NearestNeighbors(n_neighbors=self.n_neighbors,
    105                                       algorithm=self.neighbors_algorithm,

D:\anaconda\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    400         # make sure we actually converted to numeric:
    401         if dtype_numeric and array.dtype.kind == "O":
--> 402             array = array.astype(np.float64)
    403         if not allow_nd and array.ndim >= 3:
    404             raise ValueError("Found array with dim %d. %s expected <= 2."

ValueError: setting an array element with a sequence.

In [26]:

    

def Plot2D(T, title, x, y):
  fig = plt.figure()
  ax = fig.add_subplot(111)
  ax.set_title(title)
  ax.set_xlabel('Component: {0}'.format(x))
  ax.set_ylabel('Component: {0}'.format(y))
  x_size = (max(T[:,x]) - min(T[:,x])) * 0.08
  y_size = (max(T[:,y]) - min(T[:,y])) * 0.08
  # It also plots the full scatter:
  ax.scatter(T[:,x],T[:,y], marker='.', c = colors, alpha=0.7)

def Plot3D(T, title, x, y, z):
  fig = plt.figure()
  ax = fig.add_subplot(111, projection = '3d')
  ax.set_title(title)
  ax.set_xlabel('Component: {0}'.format(x))
  ax.set_ylabel('Component: {0}'.format(y))
  ax.set_zlabel('Component: {0}'.format(z))
  x_size = (max(T[:,x]) - min(T[:,x])) * 0.08
  y_size = (max(T[:,y]) - min(T[:,y])) * 0.08
  z_size = (max(T[:,z]) - min(T[:,z])) * 0.08
  # It also plots the full scatter:
  ax.scatter(T[:,x],T[:,y],T[:,z], marker='.', c = colors, alpha=0.65)


    

In [27]:

    

Plot2D(Z, "Isomap transformed data, 2D", 0, 1)
Plot3D(Z, "Isomap transformed data 3D", 0, 1, 2)


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-27-79e5b18494bd> in <module>()
----> 1 Plot2D(Z, "Isomap transformed data, 2D", 0, 1)
      2 Plot3D(Z, "Isomap transformed data 3D", 0, 1, 2)

NameError: name 'Z' is not defined

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