def wda_pre(data_train_center, data_test_center, y_label, direction, reg=100): P0 = np.random.randn(data_train_center.shape[1], direction) P0 /= np.sqrt(np.sum(P0**2, 0, keepdims=True)) Pwda, projwda = wda(data_train_center, y_label, direction, reg, k=10, maxiter=30, P0=P0) data_wda_fit = projwda(data_train_center) data_wda_test = projwda(data_test_center) return data_wda_fit, data_wda_test
def fit(self, fd_train: FDataGrid, y_train: np.array): """Fits the classifier using median of each class of the training samples Arguments : - fd_train : training data - FDataGrid - y_train : training labels - ndarray of shape (n_samples,)""" X_train = np.transpose(fd_train.data_matrix, axes=(0,2,1)) # compute mean of each class and label them mean_outliers = np.mean(X_train[np.where(y_train==1)[0]], axis=0).transpose() mean_inliers = np.mean(X_train[np.where(y_train==0)[0]], axis=0).transpose() train = np.concatenate((mean_outliers, mean_inliers)) train_lab = np.zeros(len(train)) train_lab[0:len(mean_outliers)] = 1 # project sampels P0 = np.random.randn(train.shape[1], self.p) P0 /= np.sqrt(np.sum(P0**2, 0, keepdims=True)) self.Pwda, self.projwda = wda(train, train_lab, self.p, self.reg, k=10, maxiter=100, P0=P0) train_proj = self.projwda(train) # fit classifier self.clf.fit(train_proj, train_lab)
#%% plot samples pl.figure(1) pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples') pl.legend(loc=0) pl.title('Discriminant dimensions') #%% plot distributions and loss matrix p = 2 reg = 1 k = 10 maxiter = 100 P, proj = wda(xs, ys, p, reg, k, maxiter=maxiter) #%% plot samples xsp = proj(xs) xtp = proj(xt) pl.figure(1, (10, 5)) pl.subplot(1, 2, 1) pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples') pl.legend(loc=0) pl.title('Projected training samples') pl.subplot(1, 2, 2) pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')
#%% Compute FDA p = 2 Pfda, projfda = fda(xs, ys, p) ############################################################################## # Compute Wasserstein Discriminant Analysis # ----------------------------------------- #%% Compute WDA p = 2 reg = 1e0 k = 10 maxiter = 100 Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) ############################################################################## # Plot 2D projections # ------------------- #%% plot samples xsp = projfda(xs) xtp = projfda(xt) xspw = projwda(xs) xtpw = projwda(xt) pl.figure(2)
#%% Compute FDA p = 2 Pfda, projfda = fda(xs, ys, p) ############################################################################## # Compute Wasserstein Discriminant Analysis # ----------------------------------------- #%% Compute WDA p = 2 reg = 1e0 k = 10 maxiter = 100 Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) ############################################################################## # Plot 2D projections # ------------------- #%% plot samples xsp = projfda(xs) xtp = projfda(xt) xspw = projwda(xs) xtpw = projwda(xt) pl.figure(2)