• 设为首页
  • 点击收藏
  • 手机版
    手机扫一扫访问
    迪恩网络手机版
  • 关注官方公众号
    微信扫一扫关注
    迪恩网络公众号

Python python.zip函数代码示例

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

本文整理汇总了Python中statsmodels.compat.python.zip函数的典型用法代码示例。如果您正苦于以下问题:Python zip函数的具体用法?Python zip怎么用?Python zip使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。



在下文中一共展示了zip函数的20个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: handle_missing

    def handle_missing(cls, endog, exog, missing, **kwargs):
        """
        This returns a dictionary with keys endog, exog and the keys of
        kwargs. It preserves Nones.
        """
        none_array_names = []

        if exog is not None:
            combined = (endog, exog)
            combined_names = ['endog', 'exog']
        else:
            combined = (endog,)
            combined_names = ['endog']
            none_array_names += ['exog']

        # deal with other arrays
        combined_2d = ()
        combined_2d_names = []
        if len(kwargs):
            for key, value_array in iteritems(kwargs):
                if value_array is None or value_array.ndim == 0:
                    none_array_names += [key]
                    continue
                # grab 1d arrays
                if value_array.ndim == 1:
                    combined += (value_array,)
                    combined_names += [key]
                elif value_array.squeeze().ndim == 1:
                    combined += (value_array,)
                    combined_names += [key]

                # grab 2d arrays that are _assumed_ to be symmetric
                elif value_array.ndim == 2:
                    combined_2d += (value_array,)
                    combined_2d_names += [key]
                else:
                    raise ValueError("Arrays with more than 2 dimensions "
                            "aren't yet handled")

        nan_mask = _nan_rows(*combined)
        if combined_2d:
            nan_mask = _nan_rows(*(nan_mask[:, None],) + combined_2d)

        if missing == 'raise' and np.any(nan_mask):
            raise MissingDataError("NaNs were encountered in the data")

        elif missing == 'drop':
            nan_mask = ~nan_mask
            drop_nans = lambda x: cls._drop_nans(x, nan_mask)
            drop_nans_2d = lambda x: cls._drop_nans_2d(x, nan_mask)
            combined = dict(zip(combined_names, lmap(drop_nans, combined)))
            if combined_2d:
                combined.update(dict(zip(combined_2d_names,
                                          lmap(drop_nans_2d, combined_2d))))
            if none_array_names:
                combined.update(dict(zip(none_array_names,
                                          [None] * len(none_array_names))))
            return combined, np.where(~nan_mask)[0].tolist()
        else:
            raise ValueError("missing option %s not understood" % missing)
开发者ID:andrewclegg,项目名称:statsmodels,代码行数:60,代码来源:data.py


示例2: test__reduce_dict

def test__reduce_dict():
    data = OrderedDict(zip(list(product('mf', 'oy', 'wn')), [1] * 8))
    eq(_reduce_dict(data, ('m',)), 4)
    eq(_reduce_dict(data, ('m', 'o')), 2)
    eq(_reduce_dict(data, ('m', 'o', 'w')), 1)
    data = OrderedDict(zip(list(product('mf', 'oy', 'wn')), lrange(8)))
    eq(_reduce_dict(data, ('m',)), 6)
    eq(_reduce_dict(data, ('m', 'o')), 1)
    eq(_reduce_dict(data, ('m', 'o', 'w')), 0)
开发者ID:cong1989,项目名称:statsmodels,代码行数:9,代码来源:test_mosaicplot.py


示例3: simulate

    def simulate(self):

        group_effect_var = self.dep_params[0]

        vcomp = self.dep_params[1:]
        vcomp.append(0)

        endog, exog, group, id_matrix = [], [], [], []

        for i in range(self.ngroups):

            iterators = [lrange(n) for n in self.nest_sizes]

            # The random effects
            variances = [np.sqrt(v)*np.random.normal(size=n)
                         for v,n in zip(vcomp, self.nest_sizes)]

            gpe = np.random.normal() * np.sqrt(group_effect_var)

            nest_all = []
            for j in self.nest_sizes:
                nest_all.append(set())

            for nest in product(*iterators):

                group.append(i)

                # The sum of all random effects that apply to this
                # unit
                ref = gpe + sum([v[j] for v,j in zip(variances, nest)])

                exog1 = np.random.normal(size=5)
                exog1[0] = 1
                exog.append(exog1)

                error = ref + self.error_sd * np.random.normal()

                endog1 = np.dot(exog1, self.params) + error
                endog.append(endog1)

                for j in range(len(nest)):
                    nest_all[j].add(tuple(nest[0:j+1]))

                nest1 = [len(x)-1 for x in nest_all]
                id_matrix.append(nest1[0:-1])

        self.exog = np.array(exog)
        self.endog = np.array(endog)
        self.group = np.array(group)
        self.id_matrix = np.array(id_matrix)
        self.time = np.zeros_like(self.endog)
开发者ID:0ceangypsy,项目名称:statsmodels,代码行数:51,代码来源:gee_gaussian_simulation_check.py


示例4: _band_quantiles

    def _band_quantiles(band, use_brute=use_brute, seed=seed):
        """
        Find extreme curves for a quantile band.

        From the `band` of quantiles, the associated PDF extrema values
        are computed. If `min_alpha` is not provided (single quantile value),
        `max_pdf` is set to `1E6` in order not to constrain the problem on high
        values.

        An optimization is performed per component in order to find the min and
        max curves. This is done by comparing the PDF value of a given curve
        with the band PDF.

        Parameters
        ----------
        band : array_like
            alpha values ``(max_alpha, min_alpha)`` ex: ``[0.9, 0.5]``
        use_brute : bool
            Use the brute force optimizer instead of the default differential
            evolution to find the curves. Default is False.
        seed : {None, int, np.random.RandomState}
            Seed value to pass to scipy.optimize.differential_evolution. Can
            be an integer or RandomState instance. If None, then the default
            RandomState provided by np.random is used.


        Returns
        -------
        band_quantiles : list of 1-D array
            ``(max_quantile, min_quantile)`` (2, n_features)

        """
        min_pdf = pvalues[alpha.index(band[0])]
        try:
            max_pdf = pvalues[alpha.index(band[1])]
        except IndexError:
            max_pdf = 1E6
        band = [min_pdf, max_pdf]

        pool = Pool()
        data = zip(range(dim), itertools.repeat((band, pca,
                                                 bounds, ks_gaussian,
                                                 seed, use_brute)))
        band_quantiles = pool.map(_min_max_band, data)
        pool.terminate()
        pool.close()

        band_quantiles = list(zip(*band_quantiles))

        return band_quantiles
开发者ID:ChadFulton,项目名称:statsmodels,代码行数:50,代码来源:functional.py


示例5: dataset

    def dataset(self, as_dict=False):
        """
        Returns a Python generator object for iterating over the dataset.


        Parameters
        ----------
        as_dict : bool, optional
            If as_dict is True, yield each row of observations as a dict.
            If False, yields each row of observations as a list.

        Returns
        -------
        Generator object for iterating over the dataset.  Yields each row of
        observations as a list by default.

        Notes
        -----
        If missing_values is True during instantiation of StataReader then
        observations with _StataMissingValue(s) are not filtered and should
        be handled by your applcation.
        """

        try:
            self._file.seek(self._data_location)
        except Exception:
            pass

        if as_dict:
            vars = lmap(str, self.variables())
            for i in range(len(self)):
                yield dict(zip(vars, self._next()))
        else:
            for i in range(self._header['nobs']):
                yield self._next()
开发者ID:statsmodels,项目名称:statsmodels,代码行数:35,代码来源:foreign.py


示例6: test_mosaic_simple

def test_mosaic_simple():
    # display a simple plot of 4 categories of data, splitted in four
    # levels with increasing size for each group
    # creation of the levels
    key_set = (['male', 'female'], ['old', 'adult', 'young'],
               ['worker', 'unemployed'], ['healty', 'ill'])
    # the cartesian product of all the categories is
    # the complete set of categories
    keys = list(product(*key_set))
    data = OrderedDict(zip(keys, range(1, 1 + len(keys))))
    # which colours should I use for the various categories?
    # put it into a dict
    props = {}
    #males and females in blue and red
    props[('male',)] = {'color': 'b'}
    props[('female',)] = {'color': 'r'}
    # all the groups corresponding to ill groups have a different color
    for key in keys:
        if 'ill' in key:
            if 'male' in key:
                props[key] = {'color': 'BlueViolet' , 'hatch': '+'}
            else:
                props[key] = {'color': 'Crimson' , 'hatch': '+'}
    # mosaic of the data, with given gaps and colors
    mosaic(data, gap=0.05, properties=props, axes_label=False)
    pylab.suptitle('syntetic data, 4 categories (plot 2 of 4)')
    #pylab.show()
    pylab.close('all')
开发者ID:cong1989,项目名称:statsmodels,代码行数:28,代码来源:test_mosaicplot.py


示例7: test_ic

def test_ic():
    #test information criteria
    #consistency check

    ics = [aic, aicc, bic, hqic]
    ics_sig = [aic_sigma, aicc_sigma, bic_sigma, hqic_sigma]

    for ic, ic_sig in zip(ics, ics_sig):
        assert_(ic(np.array(2),10,2).dtype == np.float, msg=repr(ic))
        assert_(ic_sig(np.array(2),10,2).dtype == np.float, msg=repr(ic_sig) )

        assert_almost_equal(ic(-10./2.*np.log(2.),10,2)/10,
                            ic_sig(2, 10, 2),
                            decimal=14)

        assert_almost_equal(ic_sig(np.log(2.),10,2, islog=True),
                            ic_sig(2, 10, 2),
                            decimal=14)


    #examples penalty directly from formula
    n, k = 10, 2
    assert_almost_equal(aic(0, 10, 2), 2*k, decimal=14)
    #next see Wikipedia
    assert_almost_equal(aicc(0, 10, 2),
                        aic(0, n, k) + 2*k*(k+1.)/(n-k-1.), decimal=14)
    assert_almost_equal(bic(0, 10, 2), np.log(n)*k, decimal=14)
    assert_almost_equal(hqic(0, 10, 2), 2*np.log(np.log(n))*k, decimal=14)
开发者ID:0ceangypsy,项目名称:statsmodels,代码行数:28,代码来源:test_eval_measures.py


示例8: pooled_odds_ratio

    def pooled_odds_ratio(self, tables):
        """
        Returns the pooled odds ratio for a list of 2x2 tables.

        The pooled odds ratio is the inverse variance weighted average
        of the sample odds ratios of the tables.
        """

        if len(tables) == 0:
            return 1.

        # Get the sampled odds ratios and variances
        log_oddsratio, var = [], []
        for table in tables:
            lor = np.log(table[1, 1]) + np.log(table[0, 0]) -\
                np.log(table[0, 1]) - np.log(table[1, 0])
            log_oddsratio.append(lor)
            var.append((1 / table.astype(np.float64)).sum())

        # Calculate the inverse variance weighted average
        wts = [1 / v for v in var]
        wtsum = sum(wts)
        wts = [w / wtsum for w in wts]
        log_pooled_or = sum([w * e for w, e in zip(wts, log_oddsratio)])

        return np.exp(log_pooled_or)
开发者ID:Bonfils-ebu,项目名称:statsmodels,代码行数:26,代码来源:cov_struct.py


示例9: test_freq_to_period

def test_freq_to_period():
    from pandas.tseries.frequencies import to_offset
    freqs = ['A', 'AS-MAR', 'Q', 'QS', 'QS-APR', 'W', 'W-MON', 'B']
    expected = [1, 1, 4, 4, 4, 52, 52, 52]
    for i, j in zip(freqs, expected):
        assert_equal(tools.freq_to_period(i), j)
        assert_equal(tools.freq_to_period(to_offset(i)), j)
开发者ID:0ceangypsy,项目名称:statsmodels,代码行数:7,代码来源:test_tsa_tools.py


示例10: evaluate

 def evaluate(self, xeval, order=None):
     xeval = self._transform(xeval)
     if order is None:
         order = len(self.polys)
     res = sum(c*p(xeval) for c, p in list(zip(self.coeffs, self.polys))[:order])
     res = self._correction(res)
     return res
开发者ID:dieterv77,项目名称:statsmodels,代码行数:7,代码来源:densityorthopoly.py


示例11: as_string

    def as_string(self, output_format='txt', **fmt_dict):
        """Return string: the formatted row.
        This is the default formatter for rows.
        Override this to get different formatting.
        A row formatter must accept as arguments
        a row (self) and an output format,
        one of ('html', 'txt', 'csv', 'latex').
        """
        fmt = self._get_fmt(output_format, **fmt_dict)

        # get column widths
        try:
            colwidths = self.table.get_colwidths(output_format, **fmt)
        except AttributeError:
            colwidths = fmt.get('colwidths')
        if colwidths is None:
            colwidths = (0,) * len(self)

        colsep = fmt['colsep']
        row_pre = fmt.get('row_pre', '')
        row_post = fmt.get('row_post', '')
        formatted_cells = []
        for cell, width in zip(self, colwidths):
            content = cell.format(width, output_format=output_format, **fmt)
            formatted_cells.append(content)
        formatted_row = row_pre + colsep.join(formatted_cells) + row_post
        formatted_row = self._decorate_below(formatted_row, output_format,
                                             **fmt)
        return formatted_row
开发者ID:ChadFulton,项目名称:statsmodels,代码行数:29,代码来源:table.py


示例12: plot2d

    def plot2d(self,ix=0,iy=1,clf=True):
        """
        Generates a 2-dimensional plot of the data set and principle components
        using matplotlib.

        ix specifies which p-dimension to put on the x-axis of the plot
        and iy specifies which to put on the y-axis (0-indexed)
        """
        import matplotlib.pyplot as plt
        x,y=self.N[:,ix],self.N[:,iy]
        if clf:
            plt.clf()
        plt.scatter(x,y)
        vals,evs=self.getEigensystem()
        #evx,evy=evs[:,ix],evs[:,iy]
        xl,xu=plt.xlim()
        yl,yu=plt.ylim()
        dx,dy=(xu-xl),(yu-yl)
        for val,vec,c in zip(vals,evs.T,self._colors):
            plt.arrow(0,0,val*vec[ix],val*vec[iy],head_width=0.05*(dx*dy/4)**0.5,fc=c,ec=c)
        #plt.arrow(0,0,vals[ix]*evs[ix,ix],vals[ix]*evs[iy,ix],head_width=0.05*(dx*dy/4)**0.5,fc='g',ec='g')
        #plt.arrow(0,0,vals[iy]*evs[ix,iy],vals[iy]*evs[iy,iy],head_width=0.05*(dx*dy/4)**0.5,fc='r',ec='r')
        if self.names is not None:
            plt.xlabel('$'+self.names[ix]+'/\\sigma$')
            plt.ylabel('$'+self.names[iy]+'/\\sigma$')
开发者ID:bashtage,项目名称:statsmodels,代码行数:25,代码来源:pca.py


示例13: test_recursive_split

def test_recursive_split():
    keys = list(product('mf'))
    data = OrderedDict(zip(keys, [1] * len(keys)))
    res = _hierarchical_split(data, gap=0)
    assert_(list(iterkeys(res)) == keys)
    res[('m',)] = (0.0, 0.0, 0.5, 1.0)
    res[('f',)] = (0.5, 0.0, 0.5, 1.0)
    keys = list(product('mf', 'yao'))
    data = OrderedDict(zip(keys, [1] * len(keys)))
    res = _hierarchical_split(data, gap=0)
    assert_(list(iterkeys(res)) == keys)
    res[('m', 'y')] = (0.0, 0.0, 0.5, 1 / 3)
    res[('m', 'a')] = (0.0, 1 / 3, 0.5, 1 / 3)
    res[('m', 'o')] = (0.0, 2 / 3, 0.5, 1 / 3)
    res[('f', 'y')] = (0.5, 0.0, 0.5, 1 / 3)
    res[('f', 'a')] = (0.5, 1 / 3, 0.5, 1 / 3)
    res[('f', 'o')] = (0.5, 2 / 3, 0.5, 1 / 3)
开发者ID:cong1989,项目名称:statsmodels,代码行数:17,代码来源:test_mosaicplot.py


示例14: test_freq_to_period

def test_freq_to_period():
    from pandas.tseries.frequencies import to_offset

    freqs = ["A", "AS-MAR", "Q", "QS", "QS-APR", "W", "W-MON", "B", "D", "H"]
    expected = [1, 1, 4, 4, 4, 52, 52, 5, 7, 24]
    for i, j in zip(freqs, expected):
        assert_equal(tools.freq_to_period(i), j)
        assert_equal(tools.freq_to_period(to_offset(i)), j)
开发者ID:phobson,项目名称:statsmodels,代码行数:8,代码来源:test_tsa_tools.py


示例15: setup_class

 def setup_class(cls):
     data = sm.datasets.macrodata.load_pandas()
     cls.macro_df = data.data[['year', 'quarter', 'realgdp', 'cpi']]
     cls.random_data = np.random.randn(100)
     index = [str(int(yr)) + '-Q' + str(int(qu))
              for yr, qu in zip(cls.macro_df.year, cls.macro_df.quarter)]
     cls.macro_df.index = index
     cls.series = cls.macro_df.cpi
开发者ID:ChadFulton,项目名称:statsmodels,代码行数:8,代码来源:test_tsa_tools.py


示例16: contrast_labels

def contrast_labels(contrasts, names, reverse=False):
    if reverse:
        sl = slice(None, None, -1)
    else:
        sl = slice(None)
    labels = [''.join(['%s%s' % (signstr(c, noplus=True),v)
                          for c,v in zip(row, names)[sl] if c != 0])
                             for row in contrasts]
    return labels
开发者ID:haribharadwaj,项目名称:statsmodels,代码行数:9,代码来源:contrast_tools.py


示例17: variables

 def variables(self):
     """
     Returns a list of the dataset's StataVariables objects.
     """
     return lmap(_StataVariable, zip(lrange(self._header['nvar']),
         self._header['typlist'], self._header['varlist'],
         self._header['srtlist'],
         self._header['fmtlist'], self._header['lbllist'],
         self._header['vlblist']))
开发者ID:statsmodels,项目名称:statsmodels,代码行数:9,代码来源:foreign.py


示例18: setUpClass

 def setUpClass(cls):
     data = sm.datasets.macrodata.load()
     cls.macro_df = pd.DataFrame.from_records(data.data)
     cls.macro_df = cls.macro_df[['year', 'quarter', 'realgdp', 'cpi']]
     cls.macro_data = cls.macro_df.to_records(index=False)
     cls.random_data = np.random.randn(100)
     index = [str(int(yr)) + '-Q' + str(int(qu))
              for yr, qu in zip(cls.macro_df.year, cls.macro_df.quarter)]
     cls.macro_df.index = index
     cls.series = cls.macro_df.cpi
开发者ID:bert9bert,项目名称:statsmodels,代码行数:10,代码来源:test_tsa_tools.py


示例19: setUpClass

 def setUpClass(cls):
     data = sm.datasets.macrodata.load()
     cls.macro_data = data.data[["year", "quarter", "realgdp", "cpi"]]
     cls.random_data = np.random.randn(100)
     year = cls.macro_data["year"]
     quarter = cls.macro_data["quarter"]
     cls.macro_df = pd.DataFrame.from_records(cls.macro_data)
     index = [str(int(yr)) + "-Q" + str(int(qu)) for yr, qu in zip(cls.macro_df.year, cls.macro_df.quarter)]
     cls.macro_df.index = index
     cls.series = cls.macro_df.cpi
开发者ID:phobson,项目名称:statsmodels,代码行数:10,代码来源:test_tsa_tools.py


示例20: _categories_level

def _categories_level(keys):
    """use the Ordered dict to implement a simple ordered set
    return each level of each category
    [[key_1_level_1,key_2_level_1],[key_1_level_2,key_2_level_2]]
    """
    res = []
    for i in zip(*(keys)):
        tuplefied = _tuplify(i)
        res.append(list(OrderedDict([(j, None) for j in tuplefied])))
    return res
开发者ID:Inoryy,项目名称:statsmodels,代码行数:10,代码来源:mosaicplot.py



注:本文中的statsmodels.compat.python.zip函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。


鲜花

握手

雷人

路过

鸡蛋
该文章已有0人参与评论

请发表评论

全部评论

专题导读
上一篇:
Python python.BytesIO类代码示例发布时间:2022-05-27
下一篇:
Python python.range函数代码示例发布时间:2022-05-27
热门推荐
阅读排行榜

扫描微信二维码

查看手机版网站

随时了解更新最新资讯

139-2527-9053

在线客服(服务时间 9:00~18:00)

在线QQ客服
地址:深圳市南山区西丽大学城创智工业园
电邮:jeky_zhao#qq.com
移动电话:139-2527-9053

Powered by 互联科技 X3.4© 2001-2213 极客世界.|Sitemap