import numpy as np from sklearn import preprocessing from evaluation.compare import compare from preprocessing.PreProcessor import PreProcessor from src.filereader.FileReader import FileReader FILE_PATH = "../../resource/PostureEntry.csv" N_VALIDATIONS = 4 samples_raw, labels, label_names = FileReader.read(FILE_PATH) labels = labels[(labels == 4) | (labels == 5)] samples_raw = samples_raw[(labels == 4) | (labels == 5), :] # determine g when calibrating # determine upright angle # put threshold on angles samples_features = preprocessing.scale(samples_raw) compare(samples_features, labels, N_VALIDATIONS)
import numpy as np
from sklearn import preprocessing
from sklearn.decomposition import PCA
from evaluation.compare import compare, compare_selected, classifiers_all
from preprocessing.PreProcessor import PreProcessor
from src.filereader.FileReader import FileReader
FILE_PATH_1 = "../../resource/PostureEntry_DMP_Phil_Monday.csv"
FILE_PATH_2 = "../../resource/PostureEntry_DMP_Sergio_Monday.csv"
FILE_PATH_3 = "../../resource/PostureEntry_DMP_Ozan.csv"
N_VALIDATIONS = 30
samples_raw, labels_raw, label_names = FileReader.readAll(
[FILE_PATH_1, FILE_PATH_2, FILE_PATH_3])
scores = []
samples_filtered, labels_reduced = PreProcessor.magnitude_theta(
samples_raw, labels_raw, 5)
samples_features = preprocessing.scale(samples_filtered)
# samples_features = PCA().fit_transform(samples_features)
compare(samples_features, labels_reduced, classifiers_all, N_VALIDATIONS)
import numpy as np from sklearn import preprocessing from evaluation.compare import compare from preprocessing.PreProcessor import PreProcessor from src.filereader.FileReader import FileReader FILE_PATH = "../../resource/PostureEntry.csv" N_VALIDATIONS = 4 FILE_PATH_1 = "../../resource/PostureEntry_11_02_back_sit_stand_phil.csv" samples_raw, labels, _ = FileReader.read(FILE_PATH_1) samples_raw = samples_raw[:, 0:6] window_size = 50 window = PreProcessor.merge_window(samples_raw, window_size) # p2p = PreProcessor.peak2peak(samples_raw, window_size) labels_reduced = labels[0::window_size] labels_reduced = labels_reduced[:-1] samples_features = window samples_features = preprocessing.scale(samples_features) compare(samples_features, labels_reduced, N_VALIDATIONS)