def plot_nodes_over_data_scattermatrix_hexbin(X, Y, mdl, predictions, distances, activities, saveplot = False):
"""models_actinf.plot_nodes_over_data_scattermatrix_hexbin
Plot models nodes (if applicable) over the hexbinned data
expanding dimensions as a scattermatrix.
"""
idim = X.shape[1]
odim = Y.shape[1]
numplots = idim * odim + 2
fig = pl.figure()
fig.suptitle("Predictions over data xy scattermatrix/hexbin (%s)" % (mdl.__class__.__name__))
gs = gridspec.GridSpec(idim, odim)
figaxes = []
for i in range(idim):
figaxes.append([])
for o in range(odim):
figaxes[i].append(fig.add_subplot(gs[i,o]))
err = 0
# colsa = ["k", "r", "g", "c", "m", "y"]
# colsb = ["k", "r", "g", "c", "m", "y"]
colsa = ["k" for col in range(idim)]
colsb = ["r" for col in range(odim)]
for i in range(odim): # odim * 2
for j in range(idim):
# pl.subplot(numplots, 1, (i*idim)+j+1)
ax = figaxes[j][i]
# target = Y[h,i]
# X__ = X_[j] # X[h,j]
# err += np.sum(np.square(target - prediction))
# ax.plot(X__, [target], colsa[j] + ".", alpha=0.25, label="target_%d" % i)
# ax.plot(X__, [prediction[0,i]], colsb[j] + "o", alpha=0.25, label="pred_%d" % i)
# ax.plot(X[:,j], Y[:,i], colsa[j] + ".", alpha=0.25, label="target_%d" % i)
ax.hexbin(X[:,j], Y[:,i], gridsize = 20, alpha=0.75, cmap=pl.get_cmap("gray"))
ax.plot(X[:,j], predictions[:,i], colsb[j] + "o", alpha=0.15, label="pred_%d" % i, markersize=8)
# pred1 = mdl.filter_e.neuron(mdl.filter_e.flat_to_coords(activities_sorted[-1]))
# ax.plot(X__, [pred1], "ro", alpha=0.5)
# pred2 = mdl.filter_e.neuron(mdl.filter_e.flat_to_coords(activities_sorted[-2]))
# ax.plot(X__, [pred2], "ro", alpha=0.25)
# print("accum total err = %f" % (err / X.shape[0] / (idim * odim)))
if saveplot:
filename = "plot_nodes_over_data_scattermatrix_hexbin_%s.jpg" % (mdl.__class__.__name__,)
savefig(fig, filename)
fig.show()
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