def lda(X, Y, dim=2, **kargs):
'''Projection on to the first 2 linear discriminant components'''
print("Computing LDA projection")
try:
ldatr = ldaModel(lda.LDA(n_components=dim))
X_lda = ldatr.fit_transform(X, Y)
return ldatr, X_lda, "Linear Discriminant projection of the features"
except Exception as e:
traceback.print_exc()
def plot_simple_demo_lda():
pylab.clf()
fig = pylab.figure(num=None, figsize=(10, 4))
pylab.subplot(121)
title = "Original feature space"
pylab.title(title)
pylab.xlabel("$X_1$")
pylab.ylabel("$X_2$")
good = x1 > x2
bad = ~good
x1g = x1[good]
x2g = x2[good]
pylab.scatter(x1g, x2g, edgecolor="blue", facecolor="blue")
x1b = x1[bad]
x2b = x2[bad]
pylab.scatter(x1b, x2b, edgecolor="red", facecolor="white")
pylab.grid(True)
pylab.subplot(122)
X = np.c_[(x1, x2)]
lda_inst = lda.LDA(n_components=1)
Xtrans = lda_inst.fit_transform(X, good)
Xg = Xtrans[good]
Xb = Xtrans[bad]
pylab.scatter(Xg[:, 0],
np.zeros(len(Xg)),
edgecolor="blue",
facecolor="blue")
pylab.scatter(Xb[:, 0],
np.zeros(len(Xb)),
edgecolor="red",
facecolor="white")
title = "Transformed feature space"
pylab.title(title)
pylab.xlabel("$X'$")
fig.axes[1].get_yaxis().set_visible(False)
pylab.grid(True)
pylab.autoscale(tight=True)
filename = "lda_demo.png"
pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
