import numpy as np import random import math from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.neighbors import KNeighborsClassifier import load_data PCA_TOGGLE = True data = load_data.loadall('melspects.npz') x_tr = data['x_tr'] y_tr = data['y_tr'] x_te = data['x_te'] y_te = data['y_te'] x_cv = data['x_cv'] y_cv = data['y_cv'] print('here1', x_tr.shape) # print(y_cv) x_tr = x_tr.reshape(x_tr.shape[0], x_tr.shape[1] * x_tr.shape[2]) x_cv = x_cv.reshape(x_cv.shape[0], x_cv.shape[1] * x_cv.shape[2]) x_te = x_te.reshape(x_te.shape[0], x_te.shape[1] * x_te.shape[2]) scaler = StandardScaler() # Fit on training set only. scaler.fit(x_tr) # Apply transform to both the training set and the test set. train_sc = scaler.transform(x_tr)
def main(): ################################################# # Data stuff data = load_data.loadall('melspects.npz') x_tr = data['x_tr'] y_tr = data['y_tr'] x_te = data['x_te'] y_te = data['y_te'] x_cv = data['x_cv'] y_cv = data['y_cv'] tr_idx = np.random.permutation(len(x_tr)) te_idx = np.random.permutation(len(x_te)) cv_idx = np.random.permutation(len(x_cv)) x_tr = x_tr[tr_idx] y_tr = y_tr[tr_idx] x_te = x_te[te_idx] y_te = y_te[te_idx] x_cv = x_cv[cv_idx] y_cv = y_cv[cv_idx] x_tr = x_tr[:, :, :, np.newaxis] x_te = x_te[:, :, :, np.newaxis] x_cv = x_cv[:, :, :, np.newaxis] y_tr = np_utils.to_categorical(y_tr) y_te = np_utils.to_categorical(y_te) y_cv = np_utils.to_categorical(y_cv) # training = np.load('gtzan/gtzan_tr.npy') # x_tr = np.delete(training, -1, 1) # label_tr = training[:,-1] # test = np.load('gtzan/gtzan_te.npy') # x_te = np.delete(test, -1, 1) # label_te = test[:,-1] # cv = np.load('gtzan/gtzan_cv.npy') # x_cv = np.delete(cv, -1, 1) # label_cv = test[:,-1] # temp = np.zeros((len(label_tr),10)) # temp[np.arange(len(label_tr)),label_tr.astype(int)] = 1 # y_tr = temp # temp = np.zeros((len(label_te),10)) # temp[np.arange(len(label_te)),label_te.astype(int)] = 1 # y_te = temp # temp = np.zeros((len(label_cv),10)) # temp[np.arange(len(label_cv)),label_cv.astype(int)] = 1 # y_cv = temp # del temp ################################################# # if True: # model = keras.models.load_model('model84.082.0.h5', custom_objects={'metric': metric}) # print("Saving confusion data...") # pred = model.predict_classes(x_te, verbose=1) # cnf_matrix = confusion_matrix(np.argmax(y_te, axis=1), pred) # np.set_printoptions(precision=1) # plt.figure() # plot_confusion_matrix(cnf_matrix, classes=song_labels, normalize=True, title='Normalized confusion matrix') # print(precision_recall_fscore_support(np.argmax(y_te, axis=1),pred, average='macro')) # plt.savefig("matrix",format='png', dpi=1000) # raise SystemExit ann = model(cnn) ann.train_model(x_tr, y_tr, val_x=x_cv, val_y=y_cv, test_x=x_te, test_y=y_te)
from load_data import loadall from json import load from os.path import join, sep from sys import path import matplotlib.pyplot as plt from IPython.display import Markdown as md import numpy as np settings = load(open("foldersettings.json")) path.append(join(f"{sep}".join(settings["projectdir"]), "from_scratch")) from mystats import avg, describe_matrix if __name__ == "__main__": datadir = f"{sep}".join(settings["datadir"]) data = loadall(datadir, prefix="*ubyte*") X_train, X_test = data["i60000"], data["i10000"] y_train, y_test = data["l60000"], data["l10000"] mdobj = md(describe_matrix(X_train)) # non-square matrix # => no solution to Ax = b # What about A^T A x_hat = A^T b ? # If there would be a solution, it could be solved with: # np.linalg.solve(X_train, y_test) print(mdobj._repr_markdown_()) # 1. Visualise and clean data plt.figure() plt.imshow(X_train[0, :].reshape(28, 28), cmap="gist_yarg") plt.savefig(join("img", "example_digit.png"))
from controller.gripper.gripper_control import Gripper_Controller from controller.ur5.ur_controller import UR_Controller from controller.mini_robot_arm.RX150_driver import RX150_Driver import GPy from load_data import loadall import control as ctrl import slycot import keyboard from queue import Queue from logger_class import Logger import collections X, Y = loadall() N = X.shape[0] print(N) idx = list(range(N)) random.seed(0) random.shuffle(idx) train_idx = idx[:int(N * 0.8)] test_idx = idx[int(N * 0.8):] X_train, Y_train = X[train_idx], Y[train_idx] # kernel1 = GPy.kern.Linear(input_dim=4,ARD=True,initialize=False) # m1 = GPy.models.SparseGPRegression(X_train, Y_train[:, 0].reshape(Y_train.shape[0], 1), kernel1, num_inducing=500, initialize=False) # m1.update_model(False) # m1.initialize_parameter()