import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
from dataloader import Dataloader
from preprocess import create_table, create_test_set
from preprocess import data_preprocess, remove_class
from preprocess import resample_dataset, hotellings_t2, inspect_outliers, normalize
from preprocess import remove_outliers
# Load data
dir_path = 'Hyperspectral'
data_file = 'indian_pines.mat'
cali_file = 'calibration.mat'
labels_file = 'indian_pines_gt.mat'
dataloader = Dataloader(dir_path, data_file, cali_file, labels_file)
# Create tables, resample and create test set
X = dataloader.get_calibrated_samples()
Y = dataloader.get_labels()
W = dataloader.get_wave_lengths()
X = create_table(X)
Y = create_table(Y)
X, Y = resample_dataset(X, Y, 4.0)
X_train, Y_train, X_test, Y_test = create_test_set(X, Y, test_frac=0.30)
m, n = X.shape
print(m, n)
plt.figure(figsize=(15, 10))
for i in range(m - 1):
plt.plot(W, X[i, :])
plt.show()
def main():
# Set seed for reproducability
np.random.seed(6969)
# Preprocess
do_smoothing = False
do_subset = False
do_snv = False
do_normalize = False
# Analysis
inspection = 'processed'
show_plots = True
do_outlier_filtering = False
do_linear_class = False
do_nonlinear_class = False
# Load data
dir_path = '../datasets/indian_pines'
data_file = 'indian_pines.mat'
cali_file = 'calibration.mat'
labels_file = 'indian_pines_gt.mat'
dataloader = Dataloader(dir_path, data_file, cali_file, labels_file)
# Create tables, resample and create test set
X = dataloader.get_calibrated_samples()
Y = dataloader.get_labels()
W = dataloader.get_wave_lengths()
X = create_table(X)
Y = create_table(Y)
X, Y = resample_dataset(X, Y, 4.0)
X_train, Y_train, X_test, Y_test = create_test_set(X, Y, test_frac=0.30)
# Smoothing, subset selection, SNV
avg_window = 5
# 0-38, 42-44, 48-53, 65-73, 84-86, 91-98, 120-144, 167-169, 172-220
subset1 = np.arange(0, 38)
subset2 = np.arange(42, 44)
subset3 = np.arange(48, 53)
subset4 = np.arange(65, 73)
subset5 = np.arange(84, 86)
subset6 = np.arange(91, 98)
subset7 = np.arange(120, 144)
subset8 = np.arange(167, 169)
subset9 = np.arange(172, 220)
subset_inds = np.concatenate((subset1, subset2, subset3, subset4, subset5,
subset6, subset7, subset8, subset9))
X_train, X_test = data_preprocess(X_train, X_test, avg_window, subset_inds,
do_smoothing, do_subset, do_snv, False)
#W = dataloader.get_wave_lengths(subset_inds)
# Outlier detection
if do_outlier_filtering:
outliers = hotellings_t2(X_train,
Y_train,
0.05,
True,
False,
fig_num=3,
fig_size=(12, 6))
outliers = np.take(outliers, [2]) # 95% CI: Total 45; 2
inspect_outliers(W, X_train, outliers)
X_train, Y_train = remove_outliers(X_train, Y_train, outliers)
# Normalization
if do_normalize:
X_train = normalize(X_train)
X_test = normalize(X_test)
# PCA inspection
if inspection == 'processed':
data_inspection(W, X_train, Y_train, 17, 1, (12, 6), 'Raw Data',
'Wave lengths [nm]', 'Radiance [Wm^(-2)sr^(-1)]')
elif inspection == 'pls':
pls_inspection(X_train, Y_train, n_comps=8)
elif inspection == 'pca':
pca_inspection(X_train, Y_train, n_comps=8)
elif inspection == 'kpca':
kernel_pca_inspection(X_train, Y_train, 8, 'linear')
# Linear classification
if do_linear_class:
linear_classification(X_train,
Y_train,
X_test,
Y_test,
n_folds=5,
n_comps_max=10,
threshold=0.90,
show_plots=show_plots,
fignum=2,
figsize=(8, 6),
normalize=False)
# Non-linear classification
if do_nonlinear_class:
gamma_min = 1e-5 #1e-5
gamma_max = 3e-1 #3e-1
n_gammas = 30 #30
gammas = np.linspace(gamma_min, gamma_max, n_gammas)
best_gamma = svm_cross_validation(X_train,
Y_train,
n_folds=5,
kernel='rbf',
gammas=gammas,
show_plots=show_plots)
svm_classification(X_train,
Y_train,
X_test,
Y_test,
kernel='rbf',
gamma=best_gamma)