numpy.std - python examples

Here are the examples of the python api numpy.std taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.

145 Examples 7

3 View Complete Implementation : scaled_updates.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : SBU-BMI
    def _new_update_deltas(self, network, parameter_vws, grads):
        learning_rate = network.find_hyperparameter(["sgd_learning_rate",
                                                     "learning_rate"],
                                                    0.1)
        # HACK changes the rest of this node... mostly restructuring
        deltas = {}
        for vw, grad in zip(parameter_vws, grads):
            initial_std = np.std(vw.value)
            # prevent multiplying by 0 std
            if initial_std == 0:
                initial_std = 1.0
            factor = treeano.utils.as_fX(-learning_rate * initial_std ** 2)
            deltas[vw.variable] = factor * grad
        return treeano.UpdateDeltas(deltas)

3 View Complete Implementation : scaled_updates.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : SBU-BMI
    def _new_update_deltas(self, network, parameter_vws, grads):
        learning_rate = network.find_hyperparameter(["sgd_learning_rate",
                                                     "learning_rate"],
                                                    0.1)
        # HACK changes the rest of this node... mostly restructuring
        deltas = {}
        for vw, grad in zip(parameter_vws, grads):
            initial_std = np.std(vw.value)
            # prevent multiplying by 0 std
            if initial_std == 0:
                initial_std = 1.0
            factor = treeano.utils.as_fX(-learning_rate * initial_std ** 2)
            deltas[vw.variable] = factor * grad
        return treeano.UpdateDeltas(deltas)

3 View Complete Implementation : scaled_updates.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : SBU-BMI
    def _new_update_deltas(self, network, parameter_vws, grads):
        learning_rate = network.find_hyperparameter(["sgd_learning_rate",
                                                     "learning_rate"],
                                                    0.1)
        # HACK changes the rest of this node... mostly restructuring
        deltas = {}
        for vw, grad in zip(parameter_vws, grads):
            initial_std = np.std(vw.value)
            # prevent multiplying by 0 std
            if initial_std == 0:
                initial_std = 1.0
            factor = treeano.utils.as_fX(-learning_rate * initial_std ** 2)
            deltas[vw.variable] = factor * grad
        return treeano.UpdateDeltas(deltas)

3 View Complete Implementation : scaled_updates.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : SBU-BMI
    def _new_update_deltas(self, network, parameter_vws, grads):
        learning_rate = network.find_hyperparameter(["sgd_learning_rate",
                                                     "learning_rate"],
                                                    0.1)
        # HACK changes the rest of this node... mostly restructuring
        deltas = {}
        for vw, grad in zip(parameter_vws, grads):
            initial_std = np.std(vw.value)
            # prevent multiplying by 0 std
            if initial_std == 0:
                initial_std = 1.0
            factor = treeano.utils.as_fX(-learning_rate * initial_std ** 2)
            deltas[vw.variable] = factor * grad
        return treeano.UpdateDeltas(deltas)

3 View Complete Implementation : test_binned_statistic.py
Copyright MIT License
Author : ktraunmueller
    def test_2d_std(self):
        x = self.x
        y = self.y
        v = self.v

        stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'std', bins=5)
        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.std, bins=5)

        astert_array_almost_equal(stat1, stat2)
        astert_array_almost_equal(binx1, binx2)
        astert_array_almost_equal(biny1, biny2)

3 View Complete Implementation : test_binned_statistic.py
Copyright MIT License
Author : ktraunmueller
    def test_2d_std(self):
        x = self.x
        y = self.y
        v = self.v

        stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'std', bins=5)
        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.std, bins=5)

        astert_array_almost_equal(stat1, stat2)
        astert_array_almost_equal(binx1, binx2)
        astert_array_almost_equal(biny1, biny2)

3 View Complete Implementation : histograms.py
Copyright MIT License
Author : PacktPublishing
def _hist_bin_scott(x):
    """
    Scott histogram bin estimator.

    The binwidth is proportional to the standard deviation of the data
    and inversely proportional to the cube root of data size
    (asymptotically optimal).

    Parameters
    ----------
    x : array_like
        Input data that is to be histogrammed, trimmed to range. May not
        be empty.

    Returns
    -------
    h : An estimate of the optimal bin width for the given data.
    """
    return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x)

3 View Complete Implementation : histograms.py
Copyright MIT License
Author : PacktPublishing
def _hist_bin_scott(x):
    """
    Scott histogram bin estimator.

    The binwidth is proportional to the standard deviation of the data
    and inversely proportional to the cube root of data size
    (asymptotically optimal).

    Parameters
    ----------
    x : array_like
        Input data that is to be histogrammed, trimmed to range. May not
        be empty.

    Returns
    -------
    h : An estimate of the optimal bin width for the given data.
    """
    return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x)

3 View Complete Implementation : histograms.py
Copyright MIT License
Author : PacktPublishing
def _hist_bin_scott(x):
    """
    Scott histogram bin estimator.

    The binwidth is proportional to the standard deviation of the data
    and inversely proportional to the cube root of data size
    (asymptotically optimal).

    Parameters
    ----------
    x : array_like
        Input data that is to be histogrammed, trimmed to range. May not
        be empty.

    Returns
    -------
    h : An estimate of the optimal bin width for the given data.
    """
    return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x)

3 View Complete Implementation : histograms.py
Copyright MIT License
Author : PacktPublishing
def _hist_bin_scott(x):
    """
    Scott histogram bin estimator.

    The binwidth is proportional to the standard deviation of the data
    and inversely proportional to the cube root of data size
    (asymptotically optimal).

    Parameters
    ----------
    x : array_like
        Input data that is to be histogrammed, trimmed to range. May not
        be empty.

    Returns
    -------
    h : An estimate of the optimal bin width for the given data.
    """
    return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x)