gl.plot(lags, residuals)
##########################################################
# Plotting of the regression
plotting_3D = 1
if (plotting_3D):
# Y_data = np.sign(Y_data)
gl.scatter_3D(X_data[:, 0],
X_data[:, 1],
Y_data,
labels=["", "", ""],
legend=["Pene"],
nf=1)
grids = ul.get_grids(X_data)
z = bMA.get_plane_Z(grids[0], grids[1], params)
h = gl.plot_3D(grids[0], grids[1], z, nf=0)
###################################
##### TODO ##################3
##################################
# How it would be to take reconstruct from the diff signal
# We shift to the right to that
# v[i] = x[i] - x[i-1] and x[i]
# If we do v[j] = v[i+1] = x[i+1] - x[i]
# Then v[j] + x[i] = x[i+1]
# So then we shift to the left
#vel_shifted = np.roll(MACD_vel[:,[indx]],-1, axis = 0)
# labels = ["slices of the joint distribution","X_1","X_2"], alpha = 0.2)
#
Y = A.dot(X)
# ax3D = gl.scatter_3D(Y[0,:], Y[1,:], Y[2,:], nf = 0,
# labels = ["slices of the joint distribution","X_1","X_2"], alpha = 0.6)
######## DRAWING THE HYPERPLANE #########
v = R[0,:]
nx = 1
grid_x = np.linspace(-nx,nx,20)
grid_y = np.linspace(-nx,nx,20)
params = [0, -v[0]/v[2], -v[1]/v[2]]
zz = bMA.get_plane_Z(grid_x, grid_y, params)
xx, yy = np.meshgrid(grid_x, grid_y, sparse=True)
ax3D = gl.plot_3D(xx, yy, zz, nf = 0,
labels = ["slices of the joint distribution","X_1","X_2"], alpha = 0.8)
#### Plotting the vectors
nx = 5
v = R[0,:]
ax3D.plot([-nx*v[0], nx*v[0]],[-nx*v[1], nx*v[1]],[-nx*v[2], nx*v[2]], lw = 3)
v = R[1,:]
ax3D.plot([-nx*v[0], nx*v[0]],[-nx*v[1], nx*v[1]],[-nx*v[2], nx*v[2]], lw = 3)
v = R[2,:]
ax3D.plot([-nx*v[0], nx*v[0]],[-nx*v[1], nx*v[1]],[-nx*v[2], nx*v[2]], lw = 3)
# ax3D.axis('equal')
# ax.plot_wireframe(x, y, z, rstride=4, cstride=4, color='b', alpha=0.2)
gl.plot(lags, residuals)
############## Plotting of 3D regression ##############
plotting_3D = 1
if (plotting_3D):
# We train 3 models
# Y_data = np.sign(Y_data)
mask_X = np.sum(np.isnan(X_data), axis = 1) == 0
mask_Y = np.isnan(Y_data) == 0
mask = mask_X & mask_Y[:,0] # Mask without NaNs. This is done automatically in the OLS
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
X_datafiltered = X_data[:,[0,1]]
regr.fit(X_datafiltered[mask,:], Y_data[mask,:])
# coeffs = np.array([regr.intercept_, regr.coef_])[0]
coeffs = np.append(regr.intercept_, regr.coef_)
params = np.array(coeffs)
gl.scatter_3D(X_data[:,0],X_data[:,1], Y_data,
labels = ["","",""],
legend = ["Pene"],
nf = 1)
grids = ul.get_grids(X_data)
z = bMA.get_plane_Z(grids[0], grids[1],params)
h = gl.plot_3D(grids[0],grids[1],z, nf = 0)
# labels = ["slices of the joint distribution","X_1","X_2"], alpha = 0.2)
#
Y = A.dot(X)
# ax3D = gl.scatter_3D(Y[0,:], Y[1,:], Y[2,:], nf = 0,
# labels = ["slices of the joint distribution","X_1","X_2"], alpha = 0.6)
######## DRAWING THE HYPERPLANE #########
v = R[0,:]
nx = 1
grid_x = np.linspace(-nx,nx,20)
grid_y = np.linspace(-nx,nx,20)
params = [0, -v[0]/v[2], -v[1]/v[2]]
zz = bMA.get_plane_Z(grid_x, grid_y, params)
xx, yy = np.meshgrid(grid_x, grid_y, sparse=True)
ax3D = gl.plot_3D(xx, yy, zz, nf = 0,
labels = ["slices of the joint distribution","X_1","X_2"], alpha = 0.8)
#### Plotting the vectors
nx = 5
v = R[0,:]
ax3D.plot([-nx*v[0], nx*v[0]],[-nx*v[1], nx*v[1]],[-nx*v[2], nx*v[2]], lw = 3)
v = R[1,:]
ax3D.plot([-nx*v[0], nx*v[0]],[-nx*v[1], nx*v[1]],[-nx*v[2], nx*v[2]], lw = 3)
v = R[2,:]
ax3D.plot([-nx*v[0], nx*v[0]],[-nx*v[1], nx*v[1]],[-nx*v[2], nx*v[2]], lw = 3)
# ax3D.axis('equal')
# ax.plot_wireframe(x, y, z, rstride=4, cstride=4, color='b', alpha=0.2)