def plot_2d(Z, y, name, model_dir):
plot_dir = os.path.join(model_dir, "figures", "2dscatter")
colors = np.array(
['forestgreen', 'red', 'royalblue', 'purple', 'darkblue', 'orange'])
os.makedirs(plot_dir, exist_ok=True)
# plt.rc('text', usetex=True)
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = ['Times New Roman']
# colors = np.array(['royalblue', 'forestgreen', 'red'])
fig, ax = plt.subplots(figsize=(6, 5), dpi=200)
ax.scatter(Z[:, 0], Z[:, 1], c=colors[y], alpha=0.5)
ax.scatter(0.0, 0.0, c='black', alpha=0.8, marker='s')
# ax.arrow(0.0, 0.0, Z[:, 0], Z[:, 1])
ax.set_ylim(-1.2, 1.2)
ax.set_xlim(-1.2, 1.2)
ax.set_xticks([-1.0, -0.5, 0.0, 0.5, 1.0])
ax.set_yticks([-1.0, -0.5, 0.0, 0.5, 1.0])
ax.grid(linestyle=':')
Z, _ = F.get_n_each(Z, y, 1)
for c in np.unique(y):
ax.arrow(0,
0,
Z[c, 0],
Z[c, 1],
head_width=0.03,
head_length=0.05,
fc='k',
ec='k',
length_includes_head=True)
[tick.label.set_fontsize(24) for tick in ax.xaxis.get_major_ticks()]
[tick.label.set_fontsize(24) for tick in ax.yaxis.get_major_ticks()]
plt.savefig(os.path.join(plot_dir, "scatter2d-" + name + ".pdf"), dpi=200)
plt.close()
def plot_3d(Z, y, name, model_dir):
colors = np.array(['green', 'blue', 'red'])
savedir = os.path.join(model_dir, 'figures', '3d')
os.makedirs(savedir, exist_ok=True)
# plt.rc('text', usetex=True)
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = ['Times New Roman']
colors = np.array(['forestgreen', 'royalblue', 'brown'])
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(Z[:, 0],
Z[:, 1],
Z[:, 2],
c=colors[y],
cmap=plt.cm.Spectral,
s=200.0)
Z, _ = F.get_n_each(Z, y, 1)
for c in np.unique(y):
ax.quiver(0.0,
0.0,
0.0,
Z[c, 0],
Z[c, 1],
Z[c, 2],
length=1.0,
normalize=True,
arrow_length_ratio=0.05,
color='black')
u, v = np.mgrid[0:2 * np.pi:20j, 0:np.pi:10j]
x = np.cos(u) * np.sin(v)
y = np.sin(u) * np.sin(v)
z = np.cos(v)
ax.plot_wireframe(x, y, z, color="gray", alpha=0.5)
ax.xaxis._axinfo["grid"]['color'] = (0, 0, 0, 0.1)
ax.yaxis._axinfo["grid"]['color'] = (0, 0, 0, 0.1)
ax.zaxis._axinfo["grid"]['color'] = (0, 0, 0, 0.1)
[tick.label.set_fontsize(24) for tick in ax.xaxis.get_major_ticks()]
[tick.label.set_fontsize(24) for tick in ax.yaxis.get_major_ticks()]
[tick.label.set_fontsize(24) for tick in ax.zaxis.get_major_ticks()]
ax.view_init(20, 15)
plt.tight_layout()
fig.savefig(os.path.join(savedir, f"scatter3d-{name}.jpg"), dpi=200)
plt.close()
f"_eta{args.eta}"
f"{args.tail}")
os.makedirs(model_dir, exist_ok=True)
utils.save_params(model_dir, vars(args))
print(model_dir)
# data loading
X_train, y_train, num_classes = dataset.generate_wave(args.time,
args.samples,
args.data,
shuffle=True)
X_test, y_test, _ = dataset.generate_wave(args.time,
args.samples,
args.data,
shuffle=False)
X_translate_train, y_translate_train = F.translate1d(*F.get_n_each(
X_train, y_train, 10),
n=2,
stride=4)
X_translate_test, y_translate_test = F.translate1d(*F.get_n_each(
X_test, y_test, 10),
n=2,
stride=4)
# setup architecture
kernels = F.generate_kernel('gaussian', (args.outchannels, 1, args.ksize))
layers = [Lift1D(kernels)
] + [Fourier1D(args.layers, eta=args.eta, eps=args.eps)]
model = Architecture(layers, model_dir, num_classes)
# train/test pass
print("Forward pass - train features")
f"_layers{args.layers}"
f"_outchannels{args.outchannels}"
f"_ksize{args.ksize}"
f"_eps{args.eps}"
f"_eta{args.eta}"
f"{args.tail}")
os.makedirs(model_dir, exist_ok=True)
utils.save_params(model_dir, vars(args))
print(model_dir)
# data loading
X_train, y_train, X_test, y_test = dataset.load_MNIST(args.data_dir)
X_train, y_train = F.filter_class(X_train, y_train, args.classes, args.samples)
X_train, y_train = F.shuffle(X_train, y_train)
X_test, y_test = F.filter_class(X_test, y_test, args.classes, args.samples)
X_translate_train, y_translate_train = F.translate2d(*F.get_n_each(
X_train, y_train, 2),
n=2,
stride=4)
X_translate_test, y_translate_test = F.translate2d(*F.get_n_each(
X_test, y_test, 2),
n=2,
stride=4)
utils.save_features(model_dir, "X_train", X_train, y_train)
utils.save_features(model_dir, "X_test", X_test, y_test)
utils.save_features(model_dir, "X_translate_train", X_translate_train,
y_translate_train)
utils.save_features(model_dir, "X_translate_test", X_translate_test,
y_translate_test)
# setup architecture
kernels = F.generate_kernel('gaussian',