import numpy as np
import theano
import theano.tensor as T
theano.config.floatX = 'float32'
from utils import load_train_subjects, load_test_subjects
train_data, train_targets, train_labels = load_train_subjects([4])
rank = 2
all_subject_labels = train_labels
unique_subject_labels, label_associations = np.unique(
all_subject_labels, return_inverse=True)
rng = np.random.RandomState(42)
init_time_components = (
rng.rand(rank, train_data.shape[-1]) - 0.5
).astype(np.float32)[np.newaxis] * \
np.ones([len(unique_subject_labels), 1, 1])
init_sensor_components = (
rng.rand(rank, train_data.shape[1]) - 0.5
).astype(np.float32)[np.newaxis] * \
np.ones([len(unique_subject_labels), 1, 1])
init_offsets = np.zeros(len(unique_subject_labels))
time_components = theano.shared(init_time_components,
from sklearn.externals.joblib import Memory from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from sklearn.metrics import accuracy_score from sklearn.pipeline import make_pipeline from utils import load_train_subjects, load_test_subjects import matplotlib.pyplot as plt mem = Memory(cachedir="cache", verbose=10) load_train_subjects = mem.cache(load_train_subjects) load_test_subjects = mem.cache(load_test_subjects) all_train_data, all_train_targets, all_train_labels = load_train_subjects() all_test_data, all_test_labels = load_test_subjects() val_idx = np.where(all_train_labels == 16)[0] all_val_data = all_train_data[val_idx] all_val_targets = all_train_targets[val_idx] all_val_labels = all_train_labels[val_idx] train_idx = np.where(all_train_labels < 15)[0] all_train_data = all_train_data[train_idx] all_train_targets = all_train_targets[train_idx] all_train_labels = all_train_labels[train_idx] X_train = all_train_data y_train = all_train_targets X_val = all_val_data