Python SydneyPIDの例

コード例 #1

ファイル: test_estimators_pid.py プロジェクト: SimonStreicher/IDTxl

def test_sydney_estimator():
    # Test Sydney estimator on logical and
    pid_sydney = SydneyPID(SETTINGS)
    Z = np.logical_and(X, Y).astype(int)
    est_sydney = pid_sydney.estimate(X, Y, Z)
    assert np.isclose(0.5, est_sydney['syn_s1_s2']), (
        'Synergy is not 0.5. for Sydney estimator.')

コード例 #2

ファイル: test_estimators_pid.py プロジェクト: russelljjarvis/IDTxl

def _estimate(Z):
    """Estimate PID for a given target."""

    # Sydney estimator
    pid_sydney = SydneyPID(SETTINGS)
    pid_tartu = TartuPID(SETTINGS)
    tic = tm.time()
    est_sydney = pid_sydney.estimate(X, Y, Z)
    t_sydney = tm.time() - tic
    # Tartu estimator
    tic = tm.time()
    est_tartu = pid_tartu.estimate(X, Y, Z)
    t_tartu = tm.time() - tic

    print('\nCopied source')
    print('Estimator            Sydney\t\tTartu\n')
    print('PID evaluation       {:.3f} s\t\t{:.3f} s\n'.format(
        t_sydney, t_tartu))
    print('Uni s1               {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['unq_s1'], est_tartu['unq_s1']))
    print('Uni s2               {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['unq_s2'], est_tartu['unq_s2']))
    print('Shared s1_s2         {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['shd_s1_s2'], est_tartu['shd_s1_s2']))
    print('Synergy s1_s2        {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['syn_s1_s2'], est_tartu['syn_s1_s2']))
    return est_sydney, est_tartu

コード例 #3

ファイル: test_estimators_pid.py プロジェクト: SimonStreicher/IDTxl

def _estimate(Z):
    """Estimate PID for a given target."""

    # Sydney estimator
    pid_sydney = SydneyPID(SETTINGS)
    pid_tartu = TartuPID(SETTINGS)
    tic = tm.time()
    est_sydney = pid_sydney.estimate(X, Y, Z)
    t_sydney = tm.time() - tic
    # Tartu estimator
    tic = tm.time()
    est_tartu = pid_tartu.estimate(X, Y, Z)
    t_tartu = tm.time() - tic

    print('\nCopied source')
    print('Estimator            Sydney\t\tTartu\n')
    print('PID evaluation       {:.3f} s\t\t{:.3f} s\n'.format(t_sydney,
                                                               t_tartu))
    print('Uni s1               {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['unq_s1'],
                                                    est_tartu['unq_s1']))
    print('Uni s2               {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['unq_s2'],
                                                    est_tartu['unq_s2']))
    print('Shared s1_s2         {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['shd_s1_s2'],
                                                    est_tartu['shd_s1_s2']))
    print('Synergy s1_s2        {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['syn_s1_s2'],
                                                    est_tartu['syn_s1_s2']))
    return est_sydney, est_tartu

コード例 #4

ファイル: test_estimators_pid.py プロジェクト: oist/network-dynamics-analyzer

def test_sydney_estimator():
    # Test Sydney estimator on logical and
    pid_sydney = SydneyPID(SETTINGS)
    Z = np.logical_and(X, Y).astype(int)
    est_sydney = pid_sydney.estimate(X, Y, Z)
    assert np.isclose(
        0.5,
        est_sydney['syn_s1_s2']), ('Synergy is not 0.5. for Sydney estimator.')

コード例 #5

ファイル: test_estimators_pid.py プロジェクト: SimonStreicher/IDTxl

def test_non_binary_alphabet():
    """Test PID estimators on larger alphabets."""
    n = 1000
    alph_s1 = 5
    alph_s2 = 3
    s1 = np.random.randint(0, alph_s1, n)
    s2 = np.random.randint(0, alph_s2, n)
    target = s1 + s2
    settings = {
        'alph_s1': alph_s1,
        'alph_s2': alph_s2,
        'alph_t': np.unique(target).shape[0],
        'max_unsuc_swaps_row_parm': 60,
        'num_reps': 63,
        'max_iters': 1000
    }
    pid_sydney = SydneyPID(settings)
    pid_tartu = TartuPID(settings)

    # Sydney estimator
    tic = tm.time()
    est_sydney = pid_sydney.estimate(s1, s2, target)
    t_sydney = tm.time() - tic
    # Tartu estimator
    tic = tm.time()
    est_tartu = pid_tartu.estimate(s1, s2, target)
    t_tartu = tm.time() - tic

    print('\nInt addition - N = 1000')
    print('Estimator            Sydney\t\tTartu\n')
    print('PID evaluation       {:.3f} s\t\t{:.3f} s\n'.format(t_sydney,
                                                               t_tartu))
    print('Uni s1               {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['unq_s1'],
                                                    est_tartu['unq_s1']))
    print('Uni s2               {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['unq_s2'],
                                                    est_tartu['unq_s2']))
    print('Shared s1_s2         {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['shd_s1_s2'],
                                                    est_tartu['shd_s1_s2']))
    print('Synergy s1_s2        {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['syn_s1_s2'],
                                                    est_tartu['syn_s1_s2']))
    assert np.isclose(est_tartu['syn_s1_s2'], est_sydney['syn_s1_s2'],
                      atol=1e-03), 'Synergies are not equal.'
    assert np.isclose(est_tartu['shd_s1_s2'], est_sydney['shd_s1_s2'],
                      atol=1e-03), 'Shareds are not equal.'
    assert np.isclose(est_tartu['unq_s1'], est_sydney['unq_s1'],
                      atol=1e-03), 'Unique1 is not equal.'
    assert np.isclose(est_tartu['unq_s2'], est_sydney['unq_s2'],
                      atol=1e-03), 'Unique2 is not equal.'

コード例 #6

ファイル: test_estimators_pid.py プロジェクト: SimonStreicher/IDTxl

def test_xor_long():
    """Test PID estimation with Sydney estimator on XOR with higher N."""
    # logical AND
    n = 1000
    alph = 2
    s1 = np.random.randint(0, alph, n)
    s2 = np.random.randint(0, alph, n)
    target = np.logical_xor(s1, s2).astype(int)
    settings = {
        'alph_s1': alph,
        'alph_s2': alph,
        'alph_t': alph,
        'max_unsuc_swaps_row_parm': 3,
        'num_reps': 63,
        'max_iters': 10000
    }
    print('\n\nTesting PID estimator on binary XOR, pointset size: {0}, '
          'iterations: {1}'.format(n, settings['max_iters']))

    # Sydney estimator
    pid_sydney = SydneyPID(settings)
    pid_tartu = TartuPID(settings)
    tic = tm.time()
    est_sydney = pid_sydney.estimate(s1, s2, target)
    t_sydney = tm.time() - tic
    # Tartu estimator
    tic = tm.time()
    est_tartu = pid_tartu.estimate(s1, s2, target)
    t_tartu = tm.time() - tic

    print('\nLogical XOR - N = 1000')
    print('Estimator            Sydney\t\tTartu\n')
    print('PID evaluation       {:.3f} s\t\t{:.3f} s\n'.format(t_sydney,
                                                               t_tartu))
    print('Uni s1               {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['unq_s1'],
                                                    est_tartu['unq_s1']))
    print('Uni s2               {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['unq_s2'],
                                                    est_tartu['unq_s2']))
    print('Shared s1_s2         {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['shd_s1_s2'],
                                                    est_tartu['shd_s1_s2']))
    print('Synergy s1_s2        {0:.8f}\t\t{1:.8f}'.format(
                                                    est_sydney['syn_s1_s2'],
                                                    est_tartu['syn_s1_s2']))
    assert 0.9 < est_sydney['syn_s1_s2'] <= 1.1, (
            'Sydney estimator incorrect synergy: {0}, expected was {1}'.format(
                    est_sydney['syn_s1s2'], 0.98))
    assert 0.9 < est_tartu['syn_s1_s2'] <= 1.1, (
            'Tartu estimator incorrect synergy: {0}, expected was {1}'.format(
                    est_sydney['syn_s1s2'], 0.98))

コード例 #7

ファイル: test_estimators_pid.py プロジェクト: russelljjarvis/IDTxl

def test_non_binary_alphabet():
    """Test PID estimators on larger alphabets."""
    n = 1000
    alph_s1 = 5
    alph_s2 = 3
    s1 = np.random.randint(0, alph_s1, n)
    s2 = np.random.randint(0, alph_s2, n)
    target = s1 + s2
    settings = {
        'alph_s1': alph_s1,
        'alph_s2': alph_s2,
        'alph_t': np.unique(target).shape[0],
        'max_unsuc_swaps_row_parm': 60,
        'num_reps': 63,
        'max_iters': 1000
    }
    pid_sydney = SydneyPID(settings)
    pid_tartu = TartuPID(settings)

    # Sydney estimator
    tic = tm.time()
    est_sydney = pid_sydney.estimate(s1, s2, target)
    t_sydney = tm.time() - tic
    # Tartu estimator
    tic = tm.time()
    est_tartu = pid_tartu.estimate(s1, s2, target)
    t_tartu = tm.time() - tic

    print('\nInt addition - N = 1000')
    print('Estimator            Sydney\t\tTartu\n')
    print('PID evaluation       {:.3f} s\t\t{:.3f} s\n'.format(
        t_sydney, t_tartu))
    print('Uni s1               {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['unq_s1'], est_tartu['unq_s1']))
    print('Uni s2               {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['unq_s2'], est_tartu['unq_s2']))
    print('Shared s1_s2         {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['shd_s1_s2'], est_tartu['shd_s1_s2']))
    print('Synergy s1_s2        {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['syn_s1_s2'], est_tartu['syn_s1_s2']))
    assert np.isclose(est_tartu['syn_s1_s2'],
                      est_sydney['syn_s1_s2'],
                      atol=1e-03), 'Synergies are not equal.'
    assert np.isclose(est_tartu['shd_s1_s2'],
                      est_sydney['shd_s1_s2'],
                      atol=1e-03), 'Shareds are not equal.'
    assert np.isclose(est_tartu['unq_s1'], est_sydney['unq_s1'],
                      atol=1e-03), 'Unique1 is not equal.'
    assert np.isclose(est_tartu['unq_s2'], est_sydney['unq_s2'],
                      atol=1e-03), 'Unique2 is not equal.'

コード例 #8

ファイル: test_estimators_pid.py プロジェクト: russelljjarvis/IDTxl

def test_xor_long():
    """Test PID estimation with Sydney estimator on XOR with higher N."""
    # logical AND
    n = 1000
    alph = 2
    s1 = np.random.randint(0, alph, n)
    s2 = np.random.randint(0, alph, n)
    target = np.logical_xor(s1, s2).astype(int)
    settings = {
        'alph_s1': alph,
        'alph_s2': alph,
        'alph_t': alph,
        'max_unsuc_swaps_row_parm': 3,
        'num_reps': 63,
        'max_iters': 10000
    }
    print('\n\nTesting PID estimator on binary XOR, pointset size: {0}, '
          'iterations: {1}'.format(n, settings['max_iters']))

    # Sydney estimator
    pid_sydney = SydneyPID(settings)
    pid_tartu = TartuPID(settings)
    tic = tm.time()
    est_sydney = pid_sydney.estimate(s1, s2, target)
    t_sydney = tm.time() - tic
    # Tartu estimator
    tic = tm.time()
    est_tartu = pid_tartu.estimate(s1, s2, target)
    t_tartu = tm.time() - tic

    print('\nLogical XOR - N = 1000')
    print('Estimator            Sydney\t\tTartu\n')
    print('PID evaluation       {:.3f} s\t\t{:.3f} s\n'.format(
        t_sydney, t_tartu))
    print('Uni s1               {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['unq_s1'], est_tartu['unq_s1']))
    print('Uni s2               {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['unq_s2'], est_tartu['unq_s2']))
    print('Shared s1_s2         {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['shd_s1_s2'], est_tartu['shd_s1_s2']))
    print('Synergy s1_s2        {0:.8f}\t\t{1:.8f}'.format(
        est_sydney['syn_s1_s2'], est_tartu['syn_s1_s2']))
    assert 0.9 < est_sydney['syn_s1_s2'] <= 1.1, (
        'Sydney estimator incorrect synergy: {0}, expected was {1}'.format(
            est_sydney['syn_s1s2'], 0.98))
    assert 0.9 < est_tartu['syn_s1_s2'] <= 1.1, (
        'Tartu estimator incorrect synergy: {0}, expected was {1}'.format(
            est_sydney['syn_s1s2'], 0.98))

コード例 #9

ファイル: demo_core_estimators.py プロジェクト: SimonStreicher/IDTxl

cmi = est.estimate(source_cor, target, source_uncor)
print('Estimated CMI: {0:.5f}, expected CMI: {1:.5f}'.format(cmi[0],
                                                             expected_mi))
est = OpenCLKraskovMI(settings)
mi = est.estimate(source_cor, target)
print('Estimated MI: {0:.5f}, expected MI: {1:.5f}'.format(mi[0], expected_mi))

# Generate binary test data
alph_x = 2
alph_y = 2
alph_z = 2
x = np.random.randint(0, alph_x, n)
y = np.random.randint(0, alph_y, n)
z = np.logical_xor(x, y).astype(int)

# PID estimators
settings = {
        'alph_s1': alph_x,
        'alph_s2': alph_y,
        'alph_t': alph_z,
        'max_unsuc_swaps_row_parm': 60,
        'num_reps': 63,
        'max_iters': 100}
est_sydney = SydneyPID(settings)
est_tartu = TartuPID(settings)
pid_sydney = est_sydney.estimate(x, y, z)
print('Estimated synergy (Sydney): {0:.5f}, expected synergy: ~1'.format(
                                                    pid_sydney['syn_s1_s2']))
pid_tartu = est_tartu.estimate(x, y, z)
print('Estimated synergy (Tartu): {0:.5f}, expected synergy: ~1'.format(
                                                    pid_tartu['syn_s1_s2']))

コード例 #10

ファイル: systemtest_estimators.py プロジェクト: russelljjarvis/IDTxl

    cmi[0], expected_mi))
est = OpenCLKraskovMI(settings)
mi = est.estimate(source_cor, target)
print('Estimated MI: {0:.5f}, expected MI: {1:.5f}'.format(mi[0], expected_mi))

# Generate binary test data
alph_x = 2
alph_y = 2
alph_z = 2
x = np.random.randint(0, alph_x, n)
y = np.random.randint(0, alph_y, n)
z = np.logical_xor(x, y).astype(int)

# PID estimators
settings = {
    'alph_s1': alph_x,
    'alph_s2': alph_y,
    'alph_t': alph_z,
    'max_unsuc_swaps_row_parm': 60,
    'num_reps': 63,
    'max_iters': 100
}
est_sydney = SydneyPID(settings)
est_tartu = TartuPID(settings)
pid_sydney = est_sydney.estimate(x, y, z)
print('Estimated synergy (Sydney): {0:.5f}, expected synergy: ~1'.format(
    pid_sydney['syn_s1_s2']))
pid_tartu = est_tartu.estimate(x, y, z)
print('Estimated synergy (Tartu): {0:.5f}, expected synergy: ~1'.format(
    pid_tartu['syn_s1_s2']))