1. Create an artificial dataset

1.1 Fix random seed for reproducibility

seed = 7
np.random.seed(seed)

1.2 We need to generate two sets of data

# The first way is by using the already existing functions:
X,y = make_circles(n_samples = 3000, noise = 0.08, factor=0.3)
print(X.shape)
print(y.shape)
print(type(X))
print(type(y))

# Spliting data into train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

(3000, 2)
(3000,)
<class 'numpy.ndarray'>
<class 'numpy.ndarray'>

# The other way is by hand:
X0 = [X[i,:] for i in range(len(y)) if y[i] == 0] # returns the row where the corresponding label is 0
X1 = [X[i,:] for i in range(len(y)) if y[i] == 1] # returns the row where the corresponding label is 1
            
X0_np = np.array(X0) # convert it into an array
X1_np = np.array(X1) 

X0_train = X0_np[:1000,:].T # picked the first 1000 samples in the second axis
X0_test = X0_np[1000:,:].T # picked the remaining 500 samples in the second axis

X1_train = X1_np[:1000,:].T  # picked the first 1000 samples in the second axis
X1_test = X1_np[1000:,:].T # picked the remaining 500 samples in the second axis

X_train = np.hstack([X0_train,X1_train]).T # all training examples
y_train=np.zeros((1,2000))
y_train[0, 1000:] = 1

X_test = np.hstack([X0_test,X1_test]).T # all test examples
y_test=np.zeros((1,1000))
y_test[0, 500:] = 1