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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Binary Classification (using GPCB)
Generate Training Data
Step1:  Train the Model
Step2:  Predict
Step3:  Binary Classification (using GPC)
Generate Training Data
Redo training data such that the value of y is 1 if it belongs to class C and 0 otherwise, where y is a vector of length Cn for n training points. The first n points are for class 1, the next n points are for class 2, and so on.
Step4:  Train the Model
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Python Code:
train_size = 50
rng = np.random.RandomState(0)
x = rng.uniform(0, 5, 100)
y = np.array((x > 2.5)*2-1, dtype=int)
plt.scatter(x,y)
Explanation: Binary Classification (using GPCB)
Generate Training Data
End of explanation
k1 = SqExp(1,1)
gpcb = GPCB(k1)
gpcb.train(x,y)
Explanation: Train the Model
End of explanation
x_star = x
pi_hat_star_mean = gpcb.predict(x_star)
pi_star_mean = gpcb.predict(x_star,False)
plt.scatter(x_star,pi_hat_star_mean)
plt.scatter(x_star,pi_star_mean)
Explanation: Predict
End of explanation
x_t = x
y_t = np.array((x_t > 2.5), dtype=int)
x = np.append(x_t,x_t)
y_c1 = y_t #1 if it belongs to class 1, 0 otherwise
y_c2 = (y_t-1)*-1 #1 if it belongs to class 2, 0 otherwise
y = np.append(y_c1,y_c2)
Explanation: Binary Classification (using GPC)
Generate Training Data
Redo training data such that the value of y is 1 if it belongs to class C and 0 otherwise, where y is a vector of length Cn for n training points. The first n points are for class 1, the next n points are for class 2, and so on.
End of explanation
k1 = SqExp(1,1)
gpc= GPC(k1)
gpc.train(x,y,2)
x_star = x_t
pi_star_mean_1 = gpc.predict(x_star,1)
pi_star_mean_2 = gpc.predict(x_star,2)
plt.scatter(x_star,pi_star_mean_1)
plt.scatter(x_star,pi_star_mean_2)
lml = gpc.lml()
#print(lml)
Explanation: Train the Model
End of explanation
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