def _demo_kde():
n = 100
np.random.seed(1)
# generate some dummy data
x = np.concatenate((np.random.normal(0, 1, int(0.3 * n)),
np.random.normal(5, 1, int(0.7 * n))))[:, np.newaxis]
# append 0 label to all data as we are interested in a single class case
y = np.zeros(x.shape)
# a subset of domain of the random variable x
x_plot = np.linspace(-5, 10, 1000)[:, np.newaxis]
# generate a dummy density function to sample data from
true_dens = (0.3 * norm(0, 1).pdf(x_plot[:, 0]) +
0.7 * norm(5, 1).pdf(x_plot[:, 0]))
fig, ax = plt.subplots()
ax.fill(x_plot[:, 0],
true_dens,
fc='black',
alpha=0.2,
label='input distribution')
# thumb up rule for bandwidth selection
bandwidth = 1.06 * min(np.std(x),
iqr(x) / 1.34) * np.power(x.shape[0], -0.2)
# try different kernels and show how the look like
for kernel in ['gaussian', 'tophat', 'epanechnikov']:
kde = KernelDensityEstimate(kernel=kernel,
bandwidth=bandwidth,
num_cls=1)
kde.fit(x, y)
log_dens = kde.list_den_est[0].score_samples(x_plot)
ax.plot(x_plot[:, 0],
np.exp(log_dens),
'-',
label="kernel = '{0}'".format(kernel))
ax.text(6, 0.38, "N={0} points".format(n))
ax.legend(loc='upper left')
ax.plot(x[:, 0], -0.005 - 0.01 * np.random.random(x.shape[0]), '+k')
ax.set_xlim(-4, 9)
ax.set_ylim(-0.02, 0.4)
plt.show()
print('KDE Flows!')
return 0
def _demo_validate_data():
dim_x = 75
num_x_p = 500
num_x_n = 500
num_ch = 20
x_p_train = np.asarray(
[np.random.randn(num_x_p, dim_x) for i in range(num_ch)])
x_n_train = np.array(
[np.random.randn(num_x_p, dim_x) for i in range(num_ch)])
y_p_train = [1] * num_x_p
y_n_train = [0] * num_x_n
x_train = np.concatenate((x_n_train, x_p_train), axis=1)
y_train = np.concatenate((y_n_train, y_p_train), axis=0)
permutation = np.random.permutation(x_train.shape[1])
x_train = x_train[:, permutation, :]
y_train = y_train[permutation]
model, _ = train_pca_rda_kde_model(x_train, y_train, k_folds=10)
fig = plt.figure()
ax = fig.add_subplot(211)
x_plot = np.linspace(np.min(model.line_el[-1]), np.max(model.line_el[-1]),
1000)[:, np.newaxis]
ax.plot(model.line_el[2][y_train == 0],
-0.005 -
0.01 * np.random.random(model.line_el[2][y_train == 0].shape[0]),
'ro',
label='class(-)')
ax.plot(model.line_el[2][y_train == 1],
-0.005 -
0.01 * np.random.random(model.line_el[2][y_train == 1].shape[0]),
'go',
label='class(+)')
for idx in range(len(model.pipeline[2].list_den_est)):
log_dens = model.pipeline[2].list_den_est[idx].score_samples(x_plot)
ax.plot(x_plot[:, 0],
np.exp(log_dens),
'r-' * (idx == 0) + 'g-' * (idx == 1),
linewidth=2.0)
ax.legend(loc='upper right')
plt.title('Training Data')
plt.ylabel('p(e|l)')
plt.xlabel('scores')
# Test
x_p_test = np.asarray(
[np.random.randn(num_x_p, dim_x) for i in range(num_ch)])
x_n_test = np.array(
[np.random.randn(num_x_p, dim_x) for i in range(num_ch)])
y_p_test = [1] * num_x_p
y_n_test = [0] * num_x_n
x_test = np.concatenate((x_n_test, x_p_test), axis=1)
y_test = np.concatenate((y_n_test, y_p_test), axis=0)
permutation = np.random.permutation(x_test.shape[1])
x_test = x_test[:, permutation, :]
y_test = y_test[permutation]
model.transform(x_test)
ax.plot(model.line_el[2][y_test == 0],
-0.01 -
0.01 * np.random.random(model.line_el[2][y_test == 0].shape[0]),
'bo',
label='t_class(-)')
ax.plot(model.line_el[2][y_test == 1],
-0.01 -
0.01 * np.random.random(model.line_el[2][y_test == 1].shape[0]),
'ko',
label='t_class(+)')
bandwidth = 1.06 * min(np.std(model.line_el[2]),
iqr(model.line_el[2]) / 1.34) * np.power(
model.line_el[2].shape[0], -0.2)
test_kde = KernelDensityEstimate(bandwidth=bandwidth)
test_kde.fit(model.line_el[2], y_test)
for idx in range(len(model.pipeline[2].list_den_est)):
log_dens = test_kde.list_den_est[idx].score_samples(x_plot)
ax.plot(x_plot[:, 0],
np.exp(log_dens),
'b--' * (idx == 0) + 'k--' * (idx == 1),
linewidth=2.0)
ax.legend(loc='upper right')
plt.title('Training Data')
plt.ylabel('p(e|l)')
plt.xlabel('scores')
plt.show()
def _demo_validate_real_data():
ds_rate = 2
channel_map = [1] * 16 + [0, 0, 1, 1, 0, 1, 1, 1, 0]
data_train_folder = load_experimental_data()
mode = 'calibration'
raw_dat, stamp_time, channels, type_amp, fs = read_data_csv(
data_train_folder + '/rawdata.csv')
dat = sig_pro(raw_dat, fs=fs, k=ds_rate)
# Get data and labels
s_i, t_t_i, t_i = trigger_decoder(mode=mode,
trigger_loc=data_train_folder +
'/triggers.txt')
x_train, y_train, num_seq, _ = trial_reshaper(t_t_i,
t_i,
dat,
mode=mode,
fs=fs,
k=ds_rate,
channel_map=channel_map)
model = train_pca_rda_kde_model(x_train, y_train, k_folds=10)
fig = plt.figure()
ax = fig.add_subplot(211)
x_plot = np.linspace(np.min(model.line_el[-1]), np.max(model.line_el[-1]),
1000)[:, np.newaxis]
ax.plot(model.line_el[2][y_train == 0],
-0.005 -
0.01 * np.random.random(model.line_el[2][y_train == 0].shape[0]),
'ro',
label='class(-)')
ax.plot(model.line_el[2][y_train == 1],
-0.005 -
0.01 * np.random.random(model.line_el[2][y_train == 1].shape[0]),
'go',
label='class(+)')
for idx in range(len(model.pipeline[2].list_den_est)):
log_dens = model.pipeline[2].list_den_est[idx].score_samples(x_plot)
ax.plot(x_plot[:, 0],
np.exp(log_dens),
'r-' * (idx == 0) + 'g-' * (idx == 1),
linewidth=2.0)
ax.legend(loc='upper right')
plt.title('Training Data')
plt.ylabel('p(e|l)')
plt.xlabel('scores')
# Test
data_test_folder = load_experimental_data()
mode = 'calibration'
raw_dat, stamp_time, channels, type_amp, fs = read_data_csv(
data_test_folder + '/rawdata.csv')
dat = sig_pro(raw_dat, fs=fs, k=ds_rate)
# Get data and labels
s_i, t_t_i, t_i = trigger_decoder(mode=mode,
trigger_loc=data_test_folder +
'/triggers.txt')
x_test, y_test, num_seq, _ = trial_reshaper(t_t_i,
t_i,
dat,
mode=mode,
fs=fs,
k=ds_rate,
channel_map=channel_map)
model.transform(x_test)
ax.plot(model.line_el[2][y_test == 0],
-0.01 -
0.01 * np.random.random(model.line_el[2][y_test == 0].shape[0]),
'bo',
label='t_class(-)')
ax.plot(model.line_el[2][y_test == 1],
-0.01 -
0.01 * np.random.random(model.line_el[2][y_test == 1].shape[0]),
'ko',
label='t_class(+)')
bandwidth = 1.06 * min(np.std(model.line_el[2]),
iqr(model.line_el[2]) / 1.34) * np.power(
model.line_el[2].shape[0], -0.2)
test_kde = KernelDensityEstimate(bandwidth=bandwidth)
test_kde.fit(model.line_el[2], y_test)
for idx in range(len(model.pipeline[2].list_den_est)):
log_dens = test_kde.list_den_est[idx].score_samples(x_plot)
ax.plot(x_plot[:, 0],
np.exp(log_dens),
'b--' * (idx == 0) + 'k--' * (idx == 1),
linewidth=2.0)
ax.legend(loc='upper right')
plt.title('Training Data')
plt.ylabel('p(e|l)')
plt.xlabel('scores')
plt.show()