Vision/clusterMain.py at master · M-Morris-95/Vision · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107

from scipy.spatial.distance import pdist, squareform
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
import csv
import numpy as np
import matplotlib.pyplot as plt
# need to import data from csv to an np.array with lat long

classes = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',
               'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']

unscaled_data = np.array([[0,0,0,0]])

with open('recogntionData_2.csv') as csv_file:
    csv_reader = csv.reader(csv_file, delimiter=',')
    line_count = 0
    for row in csv_reader:
        if line_count == 0:
            #print('Column names are', {", ".join(row)})
            line_count += 1
        elif line_count == 1:
            unscaled_data = np.array([[float(row[13]), float(row[14]), classes.index(row[11]), float(row[12])]])
            line_count += 1
        else:
            unscaled_data = np.append(unscaled_data, [[float(row[13]), float(row[14]), classes.index(row[11]), float(row[12])]], axis = 0)
            line_count += 1
Data = StandardScaler().fit_transform(unscaled_data[:,:2])

# #############################################################################
# Compute DBSCAN
db = DBSCAN(eps=0.25, min_samples=10).fit(Data)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_


# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)
latlong = np.zeros([n_clusters_,2])
stddev = np.zeros([n_clusters_])
class_conf = np.zeros([35,n_clusters_])

guess = np.zeros([n_clusters_, 5])
for i in range(n_clusters_):
    temp = unscaled_data[np.where(labels==i),:]

    guess[i][0:2] = [np.mean(temp[0,:,0]),np.mean(temp[0,:,1])]
    guess[i][2]= (np.std(temp[0,:,0])+np.std(temp[0,:,1]))/2
    for j in range(35):
        class_only = temp[0,np.where(temp[0,:,2]==j),3]
        class_conf[j,i] = np.sum(class_only)/len(temp[0,:,0])

    guess[i][3] = max(class_conf[:,i])
    guess[i][4] = np.argmax(class_conf[:,i])


#print('Estimated number of clusters: %d' % n_clusters_)
#print('Estimated number of noise points: %d' % n_noise_)

# #############################################################################

# Black removed and is used for noise instead.
unique_labels = set(labels)
colors = [plt.cm.Spectral(each)
          for each in np.linspace(0, 1, len(unique_labels))]

for k, col in zip(unique_labels, colors):
    if k == -1:
        # Black used for noise.
        col = [0, 0, 0, 1]

    class_member_mask = (labels == k)

    xy = unscaled_data[class_member_mask & core_samples_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=6)

    xy = unscaled_data[class_member_mask & ~core_samples_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=6)

plt.plot(guess[0,0], guess[0,1], 'o',
         markerfacecolor=(0, 1, 0, 1), markeredgecolor='k',
         markersize=6)
plt.plot(guess[1,0], guess[1,1], 'o',
         markerfacecolor=(0, 1, 0, 1), markeredgecolor='k',
         markersize=6)

circle1 = plt.Circle((latlong[1,0], latlong[1,1]), radius=stddev[1])

circle2 = plt.Circle((latlong[0,0], latlong[0,1]), radius=stddev[0])


fig = plt.gcf()
ax = fig.gca()

ax.add_artist(circle1)
ax.add_artist(circle2)
#plt.axis('square')

#plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()

print(guess[0][:])
print(guess[1][:])