Hands-On Machine Learning for Algorithmic Trading

The input layer

The preceding architecture is designed for two-dimensional input data, X, which represent two different classes, Y. In matrix form, both X and Y are of shape N x 2, as follows:

We will generate 50,000 random samples in the form of two concentric circles with different radii using scikit-learn's make_circles function so that the classes are not linearly separable, as follows:

N = 50000factor = 0.1noise = 0.1X, y = make_circles(n_samples=N, shuffle=True,                    factor=factor, noise=noise)

We then convert the one-dimensional output into a two-dimensional array, as follows:

Y = np.zeros((N, 2))for c in [0, 1]:    Y[y == c, c] = 1'Shape of: ...