Пример #1
0
def get_features(filename, features=features):
    d = Dataset(filename)
    ev = Evaluator() 
    ncols = len(features)
    nrows = len(d['t'])
    result = np.zeros( [nrows, ncols] )
    for (idx, f) in enumerate(features):
        print "Retrieving feature ", f
        vec = ev.eval_expr(f, env = d)
        if np.any(np.isnan(vec)):
            print "Warning: NaN in", f
        elif np.any(np.isinf(vec)):
            print "Warning: inf in", f
        result[:, idx] = vec
    return result, d 
def dataset_to_feature_matrix(d, features, start_idx=None, end_idx=None): 
    ev = Evaluator() 
    ncols = len(features)
    t = d['t'][start_idx:end_idx]
    nrows = len(t)
    print "feature matrix shape:", [nrows, ncols]
    result = np.zeros( [nrows, ncols] )
    for (idx, f) in enumerate(features):
        print "Retrieving feature ", f
        vec = ev.eval_expr(f, env = d, start_idx=start_idx, end_idx=end_idx)
        if np.any(np.isnan(vec)):
            print "Warning: NaN in", f
        elif np.any(np.isinf(vec)):
            print "Warning: inf in", f
        result[:, idx] = vec
    return result