Commit bb4e067a by Alex Fout

### changed tabs to spaces

parent 855ffc8d
 ... ... @@ -31,12 +31,12 @@ def coherence(raw_matrix): def rms(raw_matrix): """ Calculates the root mean square value of a time series Calculates the root mean square value of a time series :param raw_matrix: data matrix where rows are examples and columns are raw features :type raw_matrix: ndarray :return: feature matrix where rows are examples and columns are calculated features :rtype: ndarray """ """ rmsValues = [] for i in range(raw_matrix.shape[1]): x = raw_matrix[:, i] ... ... @@ -46,12 +46,12 @@ def rms(raw_matrix): def meanAbs(raw_matrix): """ Calculates the mean of absolute values Calculates the mean of absolute values :param raw_matrix: data matrix where rows are examples and columns are raw features :type raw_matrix: ndarray :return: feature matrix where rows are examples and columns are calculated features :rtype: ndarray """ """ meanAbsValues = [] for i in range(raw_matrix.shape[1]): x = raw_matrix[:, i] ... ... @@ -61,12 +61,12 @@ def meanAbs(raw_matrix): def std(raw_matrix): """ standard deviation of a time series standard deviation of a time series :param raw_matrix: data matrix where rows are examples and columns are raw features :type raw_matrix: ndarray :return: feature matrix where rows are examples and columns are calculated features :rtype: ndarray """ """ stdValues = [] for i in range(raw_matrix.shape[1]): x = raw_matrix[:, i] ... ... @@ -76,20 +76,20 @@ def std(raw_matrix): def subBandRatio(raw_matrix, nBands=6): """ The ratio of the mean of absolute values, between adjacent columns Note: This measure was used in a paper where the columns of the matrix represent the frequency bands The ratio of the mean of absolute values, between adjacent columns Note: This measure was used in a paper where the columns of the matrix represent the frequency bands :param raw_matrix: data matrix where rows are examples and columns are raw features :type raw_matrix: ndarray :return: feature matrix where rows are examples and columns are calculated features :rtype: ndarray """ ratio = [] channel = int(raw_matrix.shape[1]/nBands) """ ratio = [] channel = int(raw_matrix.shape[1]/nBands) for i in range(channel): x = raw_matrix[:, i*nBands:(i+1)*nBands] for j in range(x.shape[1]-1): ratio.append(np.mean(np.abs(x[:,j])) / np.mean(np.abs(x[:,j+1]))) x = raw_matrix[:, i*nBands:(i+1)*nBands] for j in range(x.shape[1]-1): ratio.append(np.mean(np.abs(x[:,j])) / np.mean(np.abs(x[:,j+1]))) return np.hstack(ratio) extractors = { ... ...
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