contour.py

X = np.array([[0, 0]])
y = np.array([[1.2]])
gp = GaussianProcess(X, y)
gp.update([[1.5, -1.5]], [[2.3]]) # second data point
gp.update([[-2,1.5]], [[-1.0]]) # third data point
gp.update([[2.1,1.3]], [[-0.6]]) # fourth data point

delta = 0.05 # changes granularity of the contour map
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z = []
for i in x:
    for j in y:
        # I take only the 0th (first) element since that is the prediction
        # The second element is the uncertainty
        Z.append(gp.new_predict([[i,j]])[0])

results = np.array([Z]).reshape(len(x), len(y))
plt.figure()
CS = plt.contourf(X, Y, results.T,  cmap='RdYlBu')
plt.colorbar()
for data in gp.X:
    plt.scatter(data[0], data[1], c="g")
plt.title("Contour Map of Steph Curry's Scoring \n After 4 Data Points (Games)")
plt.xlabel("Free Throws")
plt.ylabel("Turnovers")