def convert1d(img, c):
new_img = img.reshape((img.shape[0] * img.shape[1]), img.shape[2])
new_img = new_img.transpose()
from sklearn.decomposition import PCA
pca = PCA(n_components=c)
pca.componrnt = True
new_img = pca.fit_transform(new_img)
return new_img
len1=np.count_nonzero(dataset[:,0]==1)
len2=999-len1
data1=np.empty((len1,11))
data2=np.empty((len2,11))
l1=-1
l2=-1
for i in range(999):
if dataset[i,0]==1:
l1+=1
data1[l1,:]=dataset[i,:]
else:
l2+=1
data2[l2,:]=dataset[i,:]
pca=PCA(n_components=1)
pca.componrnt= True
k=0
for i in range(1,10):
for j in range(i+1,10):
k+=1
x=np.append(data1[:,i],data2[:,i])
y=np.append(data1[:,j],data2[:,j])
z=pca.fit_transform(np.stack((x,y),axis=1))
plt.scatter(z[:len1],np.repeat(k,len1),c='r',label='1')
plt.scatter(z[len1:],np.repeat(k,len2),c='b',label='2')
plt.xlabel('Transformed component(RED=1,BLUE=2)')
plt.ylabel('combinations of features in order')
plt.title('1D PCA of combinations of features')
plt.show()