def fitJA(j, start_date_rank):
pltf.clf()
p = artists_play_inday[j]
p = p[start_date_rank:]
print p
apcount = [0] * (183 - start_date_rank)
apdate = range(start_date_rank, 183)
for i in p:
apcount[i[1] - start_date_rank] = i[0]
print apcount
d_train = np.asarray(apdate)
c_train = np.asarray(apcount)
# create matrix versions of these arrays
D_train = d_train[:, np.newaxis]
d_test_plot = np.asarray(range(start_date_rank, 244))
D_test_plot = d_test_plot[:, np.newaxis]
pltf.scatter(d_train, c_train, label="training points")
for degree in [1,2,3]:
model = make_pipeline(PolynomialFeatures(degree), Ridge())
model.fit(D_train, c_train)
c_test_plot = model.predict(D_test_plot)
pltf.plot(d_test_plot, c_test_plot, label="degree %d" % degree)
pltf.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=5, mode="expand", borderaxespad=0.)
pltf.show()
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