I have gaussian data which is:
r1=np.random.multivariate_normal(mean1, cov1, 3000)
r2=np.random.multivariate_normal(mean2, cov2, 3000)
Now I want to add class labels for these datas to train a classifier. For r1, it is class1 and for r2, it is class2. How can I add the class labels?
considering +1 for class1 as label1 and -1 for class 2 as label2:
label1 = np.ones( (r1.shape[0],1) )
label2 = np.ones( (r2.shape[0],1) ) * -1
data = np.concatenate((r1, r2))
labels = np.concatenate((label1, label2))
In case you need to shuffle the data before training and you want to keep track of which label is for which sample, first add each label to it's corresponding data:
r1 = np.append(label1, r1, axis=1)
r2 = np.append(label2, r2, axis=1)
data = np.concatenate((r1,r2))
np.random.shuffle(data)
labels = data[:,0] #extracts labels in shape of (len(labels),)which is a rank 1 array
labels = np.reshape(labels,(len(labels),1)) #fix the shape to a 1D array
R = data[:,(1,2)] #extracting inputs
