Python numpy.percentile方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def _add_bootstrapped_inputs(self, base_algorithm, batch_sample_method,
                                 nsamples, njobs_samples, percentile,
                                 ts_byrow = False, ts_weighted = False):
        assert (batch_sample_method == 'gamma') or (batch_sample_method == 'poisson')
        assert isinstance(nsamples, int)
        assert nsamples >= 1
        self.batch_sample_method = batch_sample_method
        self.nsamples = nsamples
        self.njobs_samples = _check_njobs(njobs_samples)
        if not isinstance(base_algorithm, list):
            self.base_algorithm = self._make_bootstrapped(base_algorithm, percentile,
                                                          ts_byrow, ts_weighted)
        else:
            self.base_algorithm = [ \
                self._make_bootstrapped(alg, percentile, ts_byrow, ts_weighted) \
                for alg in base_algorithm] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def test_seasonal_fdc_recorder(self):
        """
        Test the FlowDurationCurveRecorder
        """
        model = load_model("timeseries4.json")

        df = pandas.read_csv(os.path.join(os.path.dirname(__file__), 'models', 'timeseries3.csv'),
                             parse_dates=True, dayfirst=True, index_col=0)

        percentiles = np.linspace(20., 100., 5)

        summer_flows = df.loc[pandas.Timestamp("2014-06-01"):pandas.Timestamp("2014-08-31"), :]
        summer_fdc = np.percentile(summer_flows, percentiles, axis=0)

        model.run()

        rec = model.recorders["seasonal_fdc"]
        assert_allclose(rec.fdc, summer_fdc) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def reset_percentile(self, percentile=80):
        """
        Set the upper confidence bound percentile to a custom number

        Parameters
        ----------
        percentile : int [0,100]
            Percentile of the confidence interval to take.

        Returns
        -------
        self : obj
            This object
        """
        assert (percentile > 0) and (percentile < 100)
        if self.is_fitted:
            self._oracles.reset_attribute("percentile", percentile)
        self.base_algorithm.percentile = percentile
        return self 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def __init__(self, nchoices, percentile=80, fit_intercept=True,
                 lambda_=1.0, ucb_from_empty=False,
                 beta_prior='auto', smoothing=None, noise_to_smooth=True,
                 assume_unique_reward=False,
                 random_state=None, njobs=-1):
        assert (percentile > 0) and (percentile < 100)
        assert lambda_ > 0.
        base = _LogisticUCB_n_TS_single(lambda_=float(lambda_),
                                        fit_intercept=fit_intercept,
                                        alpha=float(percentile),
                                        ts=False)
        self._add_common_params(base, beta_prior, smoothing, noise_to_smooth, njobs, nchoices,
                                False, None, False, assume_unique_reward,
                                random_state, assign_algo=True, prior_def_ucb=True,
                                force_unfit_predict = ucb_from_empty)
        self.percentile = percentile 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def reset_threshold(self, threshold="auto"):
        """
        Set the adaptive threshold to a custom number

        Parameters
        ----------
        threshold : float or "auto"
            New threshold to use. If passing "auto", will set it
            to 1.5/nchoices. Note that this threshold will still be
            decayed if the object was initialized with ``decay_type="threshold"``,
            and will still be updated if initialized with ``percentile != None``.

        Returns
        -------
        self : obj
            This object
        """
        if isinstance(threshold, int):
            threshold = float(threshold)
        elif threshold == "auto":
            threshold = 1.5 / self.nchoices
        assert isinstance(threshold, float)
        self.thr = threshold
        return self 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def agg_func(request):
    agg_func_name = request.param

    if agg_func_name == "custom":
        # When using custom you assign the function rather than a string.
        agg_func_name = npy_func = custom_test_func
    elif agg_func_name == "percentile":
        agg_func_name = {
            "func": "percentile",
            "args": [95],
            "kwargs": {}
        }
        npy_func = partial(np.percentile, q=95)
    elif agg_func_name == "percentileofscore":
        agg_func_name = {
            "func": "percentileofscore",
            "kwargs": {
                "score": 0.5,
                "kind": "rank"
            }
        }
        npy_func = partial(percentileofscore_with_axis, score=0.5, kind="rank")
    else:
        npy_func = npy_funcs[agg_func_name]
    return agg_func_name, npy_func 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def analyse_percentile(cpdf, col):
        percentile = [1, 5, 25, 50, 75, 95, 99]
        r = [round(d, 3) for d in np.percentile(list(cpdf[col]), percentile)]
        print(
            "\n預測偏差分位:",
            "\n1% 分位: ",
            r[0],
            "\n5% 分位: ",
            r[1],
            "\n25% 分位: ",
            r[2],
            "\n50% 分位: ",
            r[3],
            "\n75% 分位: ",
            r[4],
            "\n95% 分位: ",
            r[5],
            "\n99% 分位: ",
            r[6],
        ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def __init__(self, img, percentiles=[1, 99]):
        """Create norm that is linear between lower and upper percentile of img
        Parameters
        ----------
        img: array_like
            Image to normalize
        percentile: array_like, default=[1,99]
            Lower and upper percentile to consider. Pixel values below will be
            set to zero, above to saturated.
        """
        assert len(percentiles) == 2
        vmin, vmax = np.percentile(img, percentiles)
        # solution for beta assumes flat spectrum at vmax
        stretch = vmax - vmin
        beta = stretch / np.sinh(1)
        super().__init__(minimum=vmin, stretch=stretch, Q=beta) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def _compute_data_weights_topk(self, opts, density_ratios):
        """Put a uniform distribution on K points with largest prob real data.

        This is a naiive heuristic which makes next GAN concentrate on those
        points of the training set, which were classified correctly with
        largest margins. I.e., out current mixture model is not capable of
        generating points looking similar to these ones.
        """
        threshold = np.percentile(density_ratios,
                                  opts["topk_constant"]*100.0)
        # Note that largest prob_real_data corresponds to smallest density
        # ratios.
        mask = density_ratios <= threshold
        data_weights = np.zeros(self._data_num)
        data_weights[mask] = 1.0 / np.sum(mask)
        return data_weights 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def bench(f_):
    timings_fwd = []
    timings_bck = []
    for _ in range(100):

        with f_ as f:
            tic = time.perf_counter()
            f.forward()
            torch.cuda.synchronize()
            toc = time.perf_counter()
            timings_fwd.append(toc - tic)

            tic = time.perf_counter()
            f.backward()
            torch.cuda.synchronize()
            toc = time.perf_counter()
            timings_bck.append(toc - tic)

    return (np.percentile(timings_fwd, [25, 50, 75]),
            np.percentile(timings_bck, [25, 50, 75])) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def scale_EVI(evi, periods, qmin=10, qmax=90):
    """ Returns EVI scaled to upper and lower quantiles

    Quantiles are calculated based on EVI within some year-to-year interval.
    As part of finding the quantiles, EVI values not within the (0, 1) range
    will be removed.

    Args:
        evi (np.ndarray): EVI values
        periods (np.ndarray): intervals of years to group and scale together
        qmin (float, optional): lower quantile for scaling (default: 10)
        qmax (float, optional): upper quantile for scaling (default: 90)

    Returns:
        np.ndarray: scaled EVI array

    """
    _evi = evi.copy()
    for u in np.unique(periods):
        index = np.where(periods == u)
        evi_min = np.percentile(evi[index], qmin)
        evi_max = np.percentile(evi[index], qmax)
        _evi[index] = (evi[index] - evi_min) / (evi_max - evi_min)

    return _evi 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False,
                              interpolation='linear'):
    """
    Private function that doesn't support extended axis or keepdims.
    These methods are extended to this function using _ureduce
    See nanpercentile for parameter usage
    """
    if axis is None or a.ndim == 1:
        part = a.ravel()
        result = _nanquantile_1d(part, q, overwrite_input, interpolation)
    else:
        result = np.apply_along_axis(_nanquantile_1d, axis, a, q,
                                     overwrite_input, interpolation)
        # apply_along_axis fills in collapsed axis with results.
        # Move that axis to the beginning to match percentile's
        # convention.
        if q.ndim != 0:
            result = np.moveaxis(result, axis, 0)

    if out is not None:
        out[...] = result
    return result 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def test_keepdims(self):
        d = np.ones((3, 5, 7, 11))
        assert_equal(np.percentile(d, 7, axis=None, keepdims=True).shape,
                     (1, 1, 1, 1))
        assert_equal(np.percentile(d, 7, axis=(0, 1), keepdims=True).shape,
                     (1, 1, 7, 11))
        assert_equal(np.percentile(d, 7, axis=(0, 3), keepdims=True).shape,
                     (1, 5, 7, 1))
        assert_equal(np.percentile(d, 7, axis=(1,), keepdims=True).shape,
                     (3, 1, 7, 11))
        assert_equal(np.percentile(d, 7, (0, 1, 2, 3), keepdims=True).shape,
                     (1, 1, 1, 1))
        assert_equal(np.percentile(d, 7, axis=(0, 1, 3), keepdims=True).shape,
                     (1, 1, 7, 1))

        assert_equal(np.percentile(d, [1, 7], axis=(0, 1, 3),
                                   keepdims=True).shape, (2, 1, 1, 7, 1))
        assert_equal(np.percentile(d, [1, 7], axis=(0, 3),
                                   keepdims=True).shape, (2, 1, 5, 7, 1)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def test_out(self):
        mat = np.random.rand(3, 3)
        nan_mat = np.insert(mat, [0, 2], np.nan, axis=1)
        resout = np.zeros(3)
        tgt = np.percentile(mat, 42, axis=1)
        res = np.nanpercentile(nan_mat, 42, axis=1, out=resout)
        assert_almost_equal(res, resout)
        assert_almost_equal(res, tgt)
        # 0-d output:
        resout = np.zeros(())
        tgt = np.percentile(mat, 42, axis=None)
        res = np.nanpercentile(nan_mat, 42, axis=None, out=resout)
        assert_almost_equal(res, resout)
        assert_almost_equal(res, tgt)
        res = np.nanpercentile(nan_mat, 42, axis=(0, 1), out=resout)
        assert_almost_equal(res, resout)
        assert_almost_equal(res, tgt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import percentile [as 別名]
def test_multiple_percentiles(self):
        perc = [50, 100]
        mat = np.ones((4, 3))
        nan_mat = np.nan * mat
        # For checking consistency in higher dimensional case
        large_mat = np.ones((3, 4, 5))
        large_mat[:, 0:2:4, :] = 0
        large_mat[:, :, 3:] *= 2
        for axis in [None, 0, 1]:
            for keepdim in [False, True]:
                with suppress_warnings() as sup:
                    sup.filter(RuntimeWarning, "All-NaN slice encountered")
                    val = np.percentile(mat, perc, axis=axis, keepdims=keepdim)
                    nan_val = np.nanpercentile(nan_mat, perc, axis=axis,
                                               keepdims=keepdim)
                    assert_equal(nan_val.shape, val.shape)

                    val = np.percentile(large_mat, perc, axis=axis,
                                        keepdims=keepdim)
                    nan_val = np.nanpercentile(large_mat, perc, axis=axis,
                                               keepdims=keepdim)
                    assert_equal(nan_val, val)

        megamat = np.ones((3, 4, 5, 6))
        assert_equal(np.nanpercentile(megamat, perc, axis=(1, 2)).shape, (2, 3, 6))