Python numpy 模块,round_() 实例源码

我们从Python开源项目中,提取了以下20个代码示例,用于说明如何使用numpy.round_()

项目:radar    作者:amoose136    | 项目源码 | 文件源码
def test_2d(self):
        # Tests mr_ on 2D arrays.
        a_1 = np.random.rand(5, 5)
        a_2 = np.random.rand(5, 5)
        m_1 = np.round_(np.random.rand(5, 5), 0)
        m_2 = np.round_(np.random.rand(5, 5), 0)
        b_1 = masked_array(a_1, mask=m_1)
        b_2 = masked_array(a_2, mask=m_2)
        # append columns
        d = mr_['1', b_1, b_2]
        self.assertTrue(d.shape == (5, 10))
        assert_array_equal(d[:, :5], b_1)
        assert_array_equal(d[:, 5:], b_2)
        assert_array_equal(d.mask, np.r_['1', m_1, m_2])
        d = mr_[b_1, b_2]
        self.assertTrue(d.shape == (10, 5))
        assert_array_equal(d[:5,:], b_1)
        assert_array_equal(d[5:,:], b_2)
        assert_array_equal(d.mask, np.r_[m_1, m_2])
项目:krpcScripts    作者:jwvanderbeck    | 项目源码 | 文件源码
def test_2d(self):
        # Tests mr_ on 2D arrays.
        a_1 = np.random.rand(5, 5)
        a_2 = np.random.rand(5, 5)
        m_1 = np.round_(np.random.rand(5, 5), 0)
        m_2 = np.round_(np.random.rand(5, 5), 0)
        b_1 = masked_array(a_1, mask=m_1)
        b_2 = masked_array(a_2, mask=m_2)
        # append columns
        d = mr_['1', b_1, b_2]
        self.assertTrue(d.shape == (5, 10))
        assert_array_equal(d[:, :5], b_1)
        assert_array_equal(d[:, 5:], b_2)
        assert_array_equal(d.mask, np.r_['1', m_1, m_2])
        d = mr_[b_1, b_2]
        self.assertTrue(d.shape == (10, 5))
        assert_array_equal(d[:5,:], b_1)
        assert_array_equal(d[5:,:], b_2)
        assert_array_equal(d.mask, np.r_[m_1, m_2])
项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda    作者:SignalMedia    | 项目源码 | 文件源码
def test_2d(self):
        # Tests mr_ on 2D arrays.
        a_1 = np.random.rand(5, 5)
        a_2 = np.random.rand(5, 5)
        m_1 = np.round_(np.random.rand(5, 5), 0)
        m_2 = np.round_(np.random.rand(5, 5), 0)
        b_1 = masked_array(a_1, mask=m_1)
        b_2 = masked_array(a_2, mask=m_2)
        # append columns
        d = mr_['1', b_1, b_2]
        self.assertTrue(d.shape == (5, 10))
        assert_array_equal(d[:, :5], b_1)
        assert_array_equal(d[:, 5:], b_2)
        assert_array_equal(d.mask, np.r_['1', m_1, m_2])
        d = mr_[b_1, b_2]
        self.assertTrue(d.shape == (10, 5))
        assert_array_equal(d[:5,:], b_1)
        assert_array_equal(d[5:,:], b_2)
        assert_array_equal(d.mask, np.r_[m_1, m_2])
项目:aws-lambda-numpy    作者:vitolimandibhrata    | 项目源码 | 文件源码
def test_2d(self):
        # Tests mr_ on 2D arrays.
        a_1 = np.random.rand(5, 5)
        a_2 = np.random.rand(5, 5)
        m_1 = np.round_(np.random.rand(5, 5), 0)
        m_2 = np.round_(np.random.rand(5, 5), 0)
        b_1 = masked_array(a_1, mask=m_1)
        b_2 = masked_array(a_2, mask=m_2)
        # append columns
        d = mr_['1', b_1, b_2]
        self.assertTrue(d.shape == (5, 10))
        assert_array_equal(d[:, :5], b_1)
        assert_array_equal(d[:, 5:], b_2)
        assert_array_equal(d.mask, np.r_['1', m_1, m_2])
        d = mr_[b_1, b_2]
        self.assertTrue(d.shape == (10, 5))
        assert_array_equal(d[:5,:], b_1)
        assert_array_equal(d[5:,:], b_2)
        assert_array_equal(d.mask, np.r_[m_1, m_2])
项目:lambda-numba    作者:rlhotovy    | 项目源码 | 文件源码
def test_2d(self):
        # Tests mr_ on 2D arrays.
        a_1 = np.random.rand(5, 5)
        a_2 = np.random.rand(5, 5)
        m_1 = np.round_(np.random.rand(5, 5), 0)
        m_2 = np.round_(np.random.rand(5, 5), 0)
        b_1 = masked_array(a_1, mask=m_1)
        b_2 = masked_array(a_2, mask=m_2)
        # append columns
        d = mr_['1', b_1, b_2]
        self.assertTrue(d.shape == (5, 10))
        assert_array_equal(d[:, :5], b_1)
        assert_array_equal(d[:, 5:], b_2)
        assert_array_equal(d.mask, np.r_['1', m_1, m_2])
        d = mr_[b_1, b_2]
        self.assertTrue(d.shape == (10, 5))
        assert_array_equal(d[:5,:], b_1)
        assert_array_equal(d[5:,:], b_2)
        assert_array_equal(d.mask, np.r_[m_1, m_2])
项目:deliver    作者:orchestor    | 项目源码 | 文件源码
def test_2d(self):
        # Tests mr_ on 2D arrays.
        a_1 = np.random.rand(5, 5)
        a_2 = np.random.rand(5, 5)
        m_1 = np.round_(np.random.rand(5, 5), 0)
        m_2 = np.round_(np.random.rand(5, 5), 0)
        b_1 = masked_array(a_1, mask=m_1)
        b_2 = masked_array(a_2, mask=m_2)
        # append columns
        d = mr_['1', b_1, b_2]
        self.assertTrue(d.shape == (5, 10))
        assert_array_equal(d[:, :5], b_1)
        assert_array_equal(d[:, 5:], b_2)
        assert_array_equal(d.mask, np.r_['1', m_1, m_2])
        d = mr_[b_1, b_2]
        self.assertTrue(d.shape == (10, 5))
        assert_array_equal(d[:5,:], b_1)
        assert_array_equal(d[5:,:], b_2)
        assert_array_equal(d.mask, np.r_[m_1, m_2])
项目:Alfred    作者:jkachhadia    | 项目源码 | 文件源码
def test_2d(self):
        # Tests mr_ on 2D arrays.
        a_1 = np.random.rand(5, 5)
        a_2 = np.random.rand(5, 5)
        m_1 = np.round_(np.random.rand(5, 5), 0)
        m_2 = np.round_(np.random.rand(5, 5), 0)
        b_1 = masked_array(a_1, mask=m_1)
        b_2 = masked_array(a_2, mask=m_2)
        # append columns
        d = mr_['1', b_1, b_2]
        self.assertTrue(d.shape == (5, 10))
        assert_array_equal(d[:, :5], b_1)
        assert_array_equal(d[:, 5:], b_2)
        assert_array_equal(d.mask, np.r_['1', m_1, m_2])
        d = mr_[b_1, b_2]
        self.assertTrue(d.shape == (10, 5))
        assert_array_equal(d[:5,:], b_1)
        assert_array_equal(d[5:,:], b_2)
        assert_array_equal(d.mask, np.r_[m_1, m_2])
项目:radar    作者:amoose136    | 项目源码 | 文件源码
def round_(a, decimals=0, out=None):
    """
    Return a copy of a, rounded to 'decimals' places.

    When 'decimals' is negative, it specifies the number of positions
    to the left of the decimal point.  The real and imaginary parts of
    complex numbers are rounded separately. Nothing is done if the
    array is not of float type and 'decimals' is greater than or equal
    to 0.

    Parameters
    ----------
    decimals : int
        Number of decimals to round to. May be negative.
    out : array_like
        Existing array to use for output.
        If not given, returns a default copy of a.

    Notes
    -----
    If out is given and does not have a mask attribute, the mask of a
    is lost!

    """
    if out is None:
        return np.round_(a, decimals, out)
    else:
        np.round_(getdata(a), decimals, out)
        if hasattr(out, '_mask'):
            out._mask = getmask(a)
        return out
项目:krpcScripts    作者:jwvanderbeck    | 项目源码 | 文件源码
def round_(a, decimals=0, out=None):
    """
    Return a copy of a, rounded to 'decimals' places.

    When 'decimals' is negative, it specifies the number of positions
    to the left of the decimal point.  The real and imaginary parts of
    complex numbers are rounded separately. Nothing is done if the
    array is not of float type and 'decimals' is greater than or equal
    to 0.

    Parameters
    ----------
    decimals : int
        Number of decimals to round to. May be negative.
    out : array_like
        Existing array to use for output.
        If not given, returns a default copy of a.

    Notes
    -----
    If out is given and does not have a mask attribute, the mask of a
    is lost!

    """
    if out is None:
        return np.round_(a, decimals, out)
    else:
        np.round_(getdata(a), decimals, out)
        if hasattr(out, '_mask'):
            out._mask = getmask(a)
        return out
项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda    作者:SignalMedia    | 项目源码 | 文件源码
def round_(a, decimals=0, out=None):
    """
    Return a copy of a, rounded to 'decimals' places.

    When 'decimals' is negative, it specifies the number of positions
    to the left of the decimal point.  The real and imaginary parts of
    complex numbers are rounded separately. Nothing is done if the
    array is not of float type and 'decimals' is greater than or equal
    to 0.

    Parameters
    ----------
    decimals : int
        Number of decimals to round to. May be negative.
    out : array_like
        Existing array to use for output.
        If not given, returns a default copy of a.

    Notes
    -----
    If out is given and does not have a mask attribute, the mask of a
    is lost!

    """
    if out is None:
        return np.round_(a, decimals, out)
    else:
        np.round_(getdata(a), decimals, out)
        if hasattr(out, '_mask'):
            out._mask = getmask(a)
        return out
项目:aws-lambda-numpy    作者:vitolimandibhrata    | 项目源码 | 文件源码
def round_(a, decimals=0, out=None):
    """
    Return a copy of a, rounded to 'decimals' places.

    When 'decimals' is negative, it specifies the number of positions
    to the left of the decimal point.  The real and imaginary parts of
    complex numbers are rounded separately. Nothing is done if the
    array is not of float type and 'decimals' is greater than or equal
    to 0.

    Parameters
    ----------
    decimals : int
        Number of decimals to round to. May be negative.
    out : array_like
        Existing array to use for output.
        If not given, returns a default copy of a.

    Notes
    -----
    If out is given and does not have a mask attribute, the mask of a
    is lost!

    """
    if out is None:
        return np.round_(a, decimals, out)
    else:
        np.round_(getdata(a), decimals, out)
        if hasattr(out, '_mask'):
            out._mask = getmask(a)
        return out
项目:arXivTimesIndicator    作者:chakki-works    | 项目源码 | 文件源码
def std_score(a):
    return np.round_(50 + 10 * (a - np.average(a)) / np.std(a))
项目:lambda-numba    作者:rlhotovy    | 项目源码 | 文件源码
def round_(a, decimals=0, out=None):
    """
    Return a copy of a, rounded to 'decimals' places.

    When 'decimals' is negative, it specifies the number of positions
    to the left of the decimal point.  The real and imaginary parts of
    complex numbers are rounded separately. Nothing is done if the
    array is not of float type and 'decimals' is greater than or equal
    to 0.

    Parameters
    ----------
    decimals : int
        Number of decimals to round to. May be negative.
    out : array_like
        Existing array to use for output.
        If not given, returns a default copy of a.

    Notes
    -----
    If out is given and does not have a mask attribute, the mask of a
    is lost!

    """
    if out is None:
        return np.round_(a, decimals, out)
    else:
        np.round_(getdata(a), decimals, out)
        if hasattr(out, '_mask'):
            out._mask = getmask(a)
        return out
项目:querygraph    作者:peter-woyzbun    | 项目源码 | 文件源码
def test_round(self):
        param_str = "round(2.45, 1) -> float"
        test_param = TemplateParameter(parameter_str=param_str, type_converter=self.type_converter)
        result = test_param.render(df=test_df)
        self.assertEquals(result, np.round_(2.45, 1))
项目:querygraph    作者:peter-woyzbun    | 项目源码 | 文件源码
def _execute(self, value, decimals):
        return np.round_(value, decimals)
项目:querygraph    作者:peter-woyzbun    | 项目源码 | 文件源码
def _execute(self, value, decimals):
        return np.round_(value, decimals)
项目:querygraph    作者:peter-woyzbun    | 项目源码 | 文件源码
def _execute(self, value, decimals):
        return np.round_(value, decimals)
项目:deliver    作者:orchestor    | 项目源码 | 文件源码
def round_(a, decimals=0, out=None):
    """
    Return a copy of a, rounded to 'decimals' places.

    When 'decimals' is negative, it specifies the number of positions
    to the left of the decimal point.  The real and imaginary parts of
    complex numbers are rounded separately. Nothing is done if the
    array is not of float type and 'decimals' is greater than or equal
    to 0.

    Parameters
    ----------
    decimals : int
        Number of decimals to round to. May be negative.
    out : array_like
        Existing array to use for output.
        If not given, returns a default copy of a.

    Notes
    -----
    If out is given and does not have a mask attribute, the mask of a
    is lost!

    """
    if out is None:
        return np.round_(a, decimals, out)
    else:
        np.round_(getdata(a), decimals, out)
        if hasattr(out, '_mask'):
            out._mask = getmask(a)
        return out
项目:Alfred    作者:jkachhadia    | 项目源码 | 文件源码
def round_(a, decimals=0, out=None):
    """
    Return a copy of a, rounded to 'decimals' places.

    When 'decimals' is negative, it specifies the number of positions
    to the left of the decimal point.  The real and imaginary parts of
    complex numbers are rounded separately. Nothing is done if the
    array is not of float type and 'decimals' is greater than or equal
    to 0.

    Parameters
    ----------
    decimals : int
        Number of decimals to round to. May be negative.
    out : array_like
        Existing array to use for output.
        If not given, returns a default copy of a.

    Notes
    -----
    If out is given and does not have a mask attribute, the mask of a
    is lost!

    """
    if out is None:
        return np.round_(a, decimals, out)
    else:
        np.round_(getdata(a), decimals, out)
        if hasattr(out, '_mask'):
            out._mask = getmask(a)
        return out
项目:AutoML-Challenge    作者:postech-mlg-exbrain    | 项目源码 | 文件源码
def _calculate_score(solution, prediction, task_type, metric=None):

    if task_type not in TASK_TYPES:
        raise NotImplementedError(task_type)

    solution = np.array(solution, dtype=np.float32)

    if task_type == MULTICLASS_CLASSIFICATION:
        # This used to crash on travis-ci; special treatment to find out why
        # it crashed!
        solution_binary = np.zeros(prediction.shape)

        for i in range(solution_binary.shape[0]):
            label = int(np.round_(solution[i]))
            solution_binary[i, label] = 1
        solution = solution_binary

    elif task_type == BINARY_CLASSIFICATION:
        solution = solution.reshape(-1, 1)
        prediction = prediction[:, 1].reshape(-1, 1)

    if solution.shape != prediction.shape:
        raise ValueError("Solution shape %s != prediction shape %s" %
                         (solution.shape, prediction.shape))

    if metric is None:
        score = dict()
        if task_type in REGRESSION_TASKS:
            cprediction = sanitize_array(prediction)
            for metric_ in REGRESSION_METRICS:
                score[metric_] = regression_metrics.calculate_score(metric_,
                                                                    solution,
                                                                    cprediction)
        else:
            csolution, cprediction = normalize_array(solution, prediction)
            for metric_ in CLASSIFICATION_METRICS:
                score[metric_] = classification_metrics.calculate_score(
                    metric_, csolution, cprediction, task_type)

        for metric_ in score:
            if np.isnan(score[metric_]):
                score[metric_] = 0


    else:
        if task_type in REGRESSION_TASKS:
            cprediction = sanitize_array(prediction)
            score = regression_metrics.calculate_score(metric,
                                                       solution,
                                                       cprediction)
        else:
            csolution, cprediction = normalize_array(solution, prediction)
            score = classification_metrics.calculate_score(metric,
                                                           csolution,
                                                           cprediction,
                                                           task=task_type)
        if np.isnan(score):
            score = 0

    return score