def gen_colormap(max_alpha, max_beta, storage, NNU_X, NNU_D, nbrs, base_path, random=False, verbose=False):
base_path = "/home/brad/11.15/"
if random:
iden = "random"
else:
nnu = NNU(max_alpha, max_beta, storage)
nnu.build_index(NNU_D)
iden = nnu.name
count = np.zeros((max_alpha, max_beta))
for alpha in range(1, max_alpha + 1):
for beta in range(1, max_beta + 1):
if not random:
nnu_nbrs = nnu.index(NNU_X, alpha=alpha, beta=beta)
else:
nnu_nbrs = []
for x in X:
idxs = np.random.permutation(len(D))[: alpha * beta]
nnu_nbrs.append(idxs[np.argmax(np.abs(np.dot(D[idxs], x)))])
pct_found = len(np.where(nnu_nbrs == nbrs)[0]) / float(len(X))
count[alpha - 1, beta - 1] = pct_found
print alpha, beta, count[alpha - 1, beta - 1]
# plt.imshow(count, interpolation='nearest', vmin=0, vmax=1)
# plt.colorbar()
# fig = plt.gcf()
# fig.set_size_inches(18.5, 10.5)
# plt.savefig(base_path + iden + 'color.png')
# plt.clf()
np.save(base_path + iden + "_count", count)
nnu_runtimes[nnu.name] = []
ABs[nnu.name] = []
for alpha, beta in zip(alphas, betas):
runtime_total = 0.0
avg_abs = []
svm_xs_tr, svm_xs_t = [], []
total_matches = 0
total_samples = 0
enc_func = partial(nnu_to_bow, alpha=alpha, beta=beta, nnu=nnu, N=N)
for i, x in enumerate(X_tr):
print i
total_samples += len(x)
nnu_nbrs, runtime, avg_ab = nnu.index(x, alpha=alpha, beta=beta, detail=True)
svm_xs_tr.append(bow(nnu_nbrs, N))
runtime_total += runtime
avg_abs.append(avg_ab)
for x in X_t:
total_samples += len(x)
nnu_nbrs, runtime, avg_ab = nnu.index(x, alpha=alpha, beta=beta, detail=True)
svm_xs_t.append(bow(nnu_nbrs, N))
runtime_total += runtime
avg_abs.append(avg_ab)
acc = util.predict_chi2(svm_xs_tr, Y_tr, svm_xs_t, Y_t)
nnu_dists[nnu.name].append(acc)
# nnu_runtimes[nnu.name].append(runtime_total)
# ABs[nnu.name].append(np.mean(avg_abs))
