df2 = mr.load_data(file2)
# concatenate such data
data = mr.concatenate_data(df1, df2)
# find trials to later separate
trials_index = mr.find_trials(data)
# separate trials
trials = mr.separate_trials(data, trials_index)
# create the label column
labels = mr.create_multi_labels(data)
# Go through each trial, reset the columns, we split from 100-300ms ((308th sample to 513th sample))
pro_trials = mr.process_trials(trials)
# Find the mean across channels
avg_trials = mr.average_trials(pro_trials)
# concatenates the average trials dataframe with labels
ml_df = mr.create_ml_df(avg_trials, labels)
# train models
X_train, X_test, y_train, y_test = mr.prepare_ml_df(ml_df)
acc_svc, precision_svc = mr.train_svc_multi(X_train, X_test, y_train,
y_test)
acc_dtc, precision_dtc = mr.train_dtc_multi(X_train, X_test, y_train,
y_test)
# concatenate such data
data = mr.concatenate_data(df1, df2)
# find trials to later separate
trials_index = mr.find_trials(data)
# separate trials
trials = mr.separate_trials(data, trials_index)
# create the label column
labels = mr.create_ic_labels(data)
# Go through each trial, reset the columns, we split from 100-300ms ((308th sample to 513th sample))
# Increase window by 50ms each try
pro_trials = mr.process_trials(trials, 250, 550)
# Find the mean across channels
avg_trials = mr.average_trials(pro_trials)
# concatenates the average trials dataframe with labels
ml_df = mr.create_ml_df(avg_trials, labels)
# train models
X_train, X_test, y_train, y_test = mr.prepare_ml_df(ml_df, scale=False)
acc_svc, precision_svc = mr.train_svc(X_train, X_test, y_train, y_test)
acc_dtc, precision_dtc = mr.train_dtc(X_train, X_test, y_train, y_test)
acc_nb, precision_nb = mr.train_nb(X_train, X_test, y_train, y_test)