How to use _mean method in lisa

Best Python code snippet using lisa_python

RE_obj_callable.py

Source:RE_obj_callable.py

1"""2Random element objects.3"""4# TODO: docstrings?5import math6import matplotlib.pyplot as plt7import numpy as np8from scipy.special import betaln, gammaln, xlog1py, xlogy9from scipy.stats._multivariate import multi_rv_generic10from thesis._deprecated.func_obj import FiniteDomainFunc11from thesis.util.generic import check_data_shape, check_valid_pmf12from thesis.util.math import simplex_round13#%% Base RE classes14class BaseRE(multi_rv_generic):15    """16    Base class for generic random element objects.17    """18    def __init__(self, rng=None):19        super().__init__(rng)  # may be None or int for legacy numpy rng20        self._data_shape = None21        self._mode = None22    @property23    def data_shape(self):24        return self._data_shape25    @property26    def mode(self):27        return self._mode28    def rvs(self, size=(), random_state=None):29        if type(size) is int:30            size = (size,)31        # elif not size == ():32        #     raise TypeError("Input 'size' must be int or ().")33        elif type(size) is not tuple:34            raise TypeError("Input 'size' must be int or tuple.")35        random_state = self._get_random_state(random_state)36        return self._rvs(size, random_state)37    def _rvs(self, size=(), random_state=None):38        raise NotImplementedError("Method must be overwritten.")39        pass40class BaseRV(BaseRE):41    """42    Base class for generic random variable (numeric) objects.43    """44    def __init__(self, rng=None):45        super().__init__(rng)46        self._mean = None47        self._cov = None48    @property49    def mean(self):50        return self._mean51    @property52    def cov(self):53        return self._cov54class DiscreteRE(BaseRE):55    """56    Base class for discrete random element objects.57    """58    def pf(self, x):59        return self.pmf(x)60    def pmf(self, x):61        x, set_shape = check_data_shape(x, self._data_shape)62        return self._pmf(x).reshape(set_shape)63    def _pmf(self, x):64        _out = []65        for x_i in x.reshape((-1,) + self._data_shape):66            _out.append(self._pmf_single(x_i))67        return np.asarray(_out)  # returned array may be flattened over 'set_shape'68    def _pmf_single(self, x):69        raise NotImplementedError("Method must be overwritten.")70        pass71class DiscreteRV(DiscreteRE, BaseRV):72    """73    Base class for discrete random variable (numeric) objects.74    """75class ContinuousRV(BaseRV):76    """77    Base class for continuous random element objects.78    """79    def pf(self, x):80        return self.pdf(x)81    def pdf(self, x):82        x, set_shape = check_data_shape(x, self._data_shape)83        return self._pdf(x).reshape(set_shape)84    def _pdf(self, x):85        _out = []86        for x_i in x.reshape((-1,) + self._data_shape):87            _out.append(self._pdf_single(x_i))88        return np.asarray(_out)  # returned array may be flattened89    def _pdf_single(self, x):90        raise NotImplementedError("Method must be overwritten.")91        pass92#%% Specific RE's93class FiniteRE(DiscreteRE):94    """95    Generic RE drawn from a finite support set using an explicitly defined PMF.96    """97    def __new__(cls, pmf, rng=None):  # TODO: function type check98        if np.issubdtype(pmf.supp.dtype, np.number):99            return super().__new__(FiniteRV)100        else:101            return super().__new__(cls)102    def __init__(self, pmf, rng=None):103        super().__init__(rng)104        self.pmf = pmf105        self._update_attr()106    @classmethod107    def gen_func(cls, supp, p, rng=None):108        p = np.asarray(p)109        pmf = FiniteDomainFunc(supp, p)110        return cls(pmf, rng)111    # Input properties112    @property113    def supp(self):114        return self._supp115    @property116    def p(self):117        return self._p118    @p.setter  # TODO: pmf setter? or just p?119    def p(self, p):120        self.pmf.val = p121        self._update_attr()122    # Attribute Updates123    def _update_attr(self):124        self._supp = self.pmf.supp125        self._p = check_valid_pmf(self.pmf(self._supp))126        self._data_shape = self.pmf.data_shape_x127        self._supp_flat = self.pmf._supp_flat128        self._mode = self.pmf.argmax129    def _rvs(self, size=(), random_state=None):130        i = random_state.choice(self._p.size, size, p=self._p.flatten())131        return self._supp_flat[i].reshape(size + self._data_shape)132    # def _pmf_single(self, x):133    #     return self._func(x)134    # def pmf(self, x):135    #     return self._func(x)136    def plot_pmf(self, ax=None):137        self.pmf.plot(ax)138class FiniteRV(FiniteRE, DiscreteRV):139    """140    Generic RV drawn from a finite support set using an explicitly defined PMF.141    """142    def _update_attr(self):143        super()._update_attr()144        self._mean = self.pmf.m1145        self._cov = self.pmf.m2c146# s = np.random.random((4, 3, 2, 2))147# pp = np.random.random((4, 3))148# pp = pp / pp.sum()149# f = FiniteRE.gen_func(s, pp)150# f.pmf(f.rvs((4,5)))151# # f.plot_pmf()152#153# s, p = np.stack(np.meshgrid([0,1],[0,1,2]), axis=-1), np.random.random((3,2))154# # s, p = ['a','b','c'], [.3,.2,.5]155# p = p / p.sum()156# f2 = FiniteRE.gen_func(s, p)157# f2.pmf(f2.rvs(4))158# f2.plot_pmf()159def _dirichlet_check_alpha_0(alpha_0):160    # alpha_0 = np.asarray(alpha_0)161    # if alpha_0.size > 1 or alpha_0 <= 0:162    #     raise ValueError("Concentration parameter must be a positive scalar.")163    alpha_0 = float(alpha_0)164    if alpha_0 <= 0:165        raise ValueError("Concentration parameter must be a positive scalar.")166    return alpha_0167def _check_func_pmf(f, full_support=False):168    if f.data_shape_y != ():169        raise ValueError("Must be scalar function.")170    if full_support and f.min <= 0:171        raise ValueError("Function range must be positive real.")172    if not full_support and f.min < 0:173        raise ValueError("Function range must be non-negative real.")174    return f175def _dirichlet_check_input(x, alpha_0, mean):176    # x = check_valid_pmf(x, shape=mean.shape)177    if not isinstance(x, type(mean)):178        raise TypeError("Input must have same function type as mean.")179    # if np.logical_and(x == 0, mean < 1 / alpha_0).any():180    if np.logical_and(x.val == 0, mean.val < 1 / alpha_0).any():181        raise ValueError(182            "Each element in 'x' must be greater than "183            "zero if the corresponding mean element is less than 1 / alpha_0."184        )185    return x186class DirichletRV(ContinuousRV):187    """188    Dirichlet random process, finite-supp realizations.189    """190    def __init__(self, alpha_0, mean, rng=None):191        super().__init__(rng)192        self._alpha_0 = _dirichlet_check_alpha_0(alpha_0)193        self._mean = _check_func_pmf(mean, full_support=True)194        self._update_attr()195    @classmethod196    def gen_func(cls, alpha_0, supp, p, rng=None):197        p = np.asarray(p)198        mean = FiniteDomainFunc(supp, p)199        return cls(alpha_0, mean, rng)200    # Input properties201    @property202    def alpha_0(self):203        return self._alpha_0204    @alpha_0.setter205    def alpha_0(self, alpha_0):206        self._alpha_0 = _dirichlet_check_alpha_0(alpha_0)207        self._update_attr()208    @property209    def mean(self):210        return self._mean211    @mean.setter212    def mean(self, mean):213        self._mean = _check_func_pmf(mean, full_support=True)214        self._update_attr()215    # Attribute Updates216    def _update_attr(self):217        self._data_shape = self._mean.set_shape218        self._data_size = math.prod(self._data_shape)219        if self._mean.min > 1 / self._alpha_0:220            self._mode = (self._mean - 1 / self._alpha_0) / (1 - self._data_size / self._alpha_0)221        else:222            # warnings.warn("Mode method currently supported for mean > 1/alpha_0 only")Myq.L223            self._mode = None  # TODO: complete with general formula224        # TODO: IMPLEMENT COV225        # self._cov = (diag_gen(self._mean) - outer_gen(self._mean, self._mean)) / (self._alpha_0 + 1)226        self._log_pdf_coef = gammaln(self._alpha_0) - np.sum(227            gammaln(self._alpha_0 * self._mean.val)228        )229    def _rvs(self, size=(), random_state=None):230        vals = random_state.dirichlet(self._alpha_0 * self._mean.val.flatten(), size).reshape(231            size + self._data_shape232        )233        if size == ():234            return FiniteDomainFunc(self.mean.supp, vals)235        else:236            return [FiniteDomainFunc(self.mean.supp, val) for val in vals]237    def pdf(self, x):  # overwrites base methods...238        x = _dirichlet_check_input(x, self._alpha_0, self._mean)239        log_pdf = self._log_pdf_coef + np.sum(240            xlogy(self._alpha_0 * self._mean.val - 1, x.val).reshape(-1, self._data_size),241            -1,242        )243        return np.exp(log_pdf)244    # def plot_pdf(self, x, ax=None):   TODO245    #246    #     if self._size in (2, 3):247    #                     if x is None:248    #                 x = simplex_grid(40, self._shape, hull_mask=(self.mean < 1 / self.alpha_0))249    #             # x = simplex_grid(n_plt, self._shape, hull_mask=(self.mean < 1 / self.alpha_0))250    #         x = simplex_grid(n_plt, self._shape, hull_mask=(self.mean < 1 / self.alpha_0))251    #         pdf_plt = self.pdf(x)252    #         x.resize(x.shape[0], self._size)253    #254    #         # pdf_plt.sum() / (n_plt ** (self._size - 1))255    #256    #         if self._size == 2:257    #             if ax is None:258    #                 _, ax = plt.subplots()259    #                 ax.set(xlabel='$x_1$', ylabel='$x_2$')260    #261    #             plt_data = ax.scatter(x[:, 0], x[:, 1], s=15, c=pdf_plt)262    #263    #             c_bar = plt.colorbar(plt_data)264    #             c_bar.set_label(r'$\mathrm{p}_\mathrm{x}(x)$')265    #266    #         elif self._size == 3:267    #             if ax is None:268    #                 _, ax = plt.subplots(subplot_kw={'projection': '3d'})269    #                 ax.view_init(35, 45)270    #                 ax.set(xlabel='$x_1$', ylabel='$x_2$', zlabel='$x_3$')271    #272    #             plt_data = ax.scatter(x[:, 0], x[:, 1], x[:, 2], s=15, c=pdf_plt)273    #274    #             c_bar = plt.colorbar(plt_data)275    #             c_bar.set_label(r'$\mathrm{p}_\mathrm{x}(x)$')276    #277    #         return plt_data278    #279    #     else:280    #         raise NotImplementedError('Plot method only supported for 2- and 3-dimensional data.')281# rng = np.random.default_rng()282# a0 = 100283# supp = list('abc')284# val = np.random.random(3)285# val = val / val.sum()286# m = FiniteDomainFunc(supp, val)287# m('a')288#289# d = Dirichlet(a0, m, rng)290# d.mean291# d.mode292# d.cov293# d.rvs()294# d.pdf(d.rvs())295def _empirical_check_n(n):296    if not isinstance(n, int) or n < 1:297        raise ValueError("Input 'n' must be a positive integer.")298    return n299def _empirical_check_input(x, n, mean):300    # x = check_valid_pmf(x, shape=mean.shape)301    if not isinstance(x, type(mean)):302        raise TypeError("Input must have same function type as mean.")303    # if (np.minimum((n * x) % 1, (-n * x) % 1) > 1e-9).any():304    if (np.minimum((n * x.val) % 1, (-n * x.val) % 1) > 1e-9).any():305        raise ValueError("Each entry in 'x' must be a multiple of 1/n.")306    return x307class EmpiricalRV(DiscreteRV):308    """309    Empirical random process, finite-supp realizations.310    """311    def __init__(self, n, mean, rng=None):312        super().__init__(rng)313        self._n = _empirical_check_n(n)314        self._mean = _check_func_pmf(mean, full_support=False)315        self._update_attr()316    @classmethod317    def gen_func(cls, n, supp, p, rng=None):318        p = np.asarray(p)319        mean = FiniteDomainFunc(supp, p)320        return cls(n, mean, rng)321    # Input properties322    @property323    def n(self):324        return self._n325    @n.setter326    def n(self, n):327        self._n = _empirical_check_n(n)328        self._update_attr()329    @property330    def mean(self):331        return self._mean332    @mean.setter333    def mean(self, mean):334        self._mean = _check_func_pmf(mean, full_support=True)335        self._update_attr()336    # Attribute Updates337    def _update_attr(self):338        self._data_shape = self._mean.set_shape339        self._data_size = self._mean.size340        self._mode = ((self._n * self._mean) // 1) + FiniteDomainFunc(341            self._mean.supp, simplex_round((self._n * self._mean.val) % 1)342        )343        # TODO: IMPLEMENT COV344        # self._cov = (diag_gen(self._mean) - outer_gen(self._mean, self._mean)) / self._n345        self._log_pmf_coef = gammaln(self._n + 1)346    def _rvs(self, size=(), random_state=None):347        vals = random_state.multinomial(self._n, self._mean.val.flatten(), size).reshape(348            size + self._data_shape349        )350        if size == ():351            return FiniteDomainFunc(self.mean.supp, vals)352        else:353            return [FiniteDomainFunc(self.mean.supp, val) for val in vals]354    def pmf(self, x):355        x = _empirical_check_input(x, self._n, self._mean)356        log_pmf = self._log_pmf_coef + (357            xlogy(self._n * x.val, self._mean.val) - gammaln(self._n * x.val + 1)358        ).reshape(-1, self._data_size).sum(axis=-1)359        return np.exp(log_pmf)360    # def plot_pmf(self, ax=None):361    #362    #     if self._size in (2, 3):363    #         x = simplex_grid(self.n, self._shape)364    #         pmf_plt = self.pmf(x)365    #         x.resize(x.shape[0], self._size)366    #367    #         if self._size == 2:368    #             if ax is None:369    #                 _, ax = plt.subplots()370    #                 ax.set(xlabel='$x_1$', ylabel='$x_2$')371    #372    #             plt_data = ax.scatter(x[:, 0], x[:, 1], s=15, c=pmf_plt)373    #374    #             c_bar = plt.colorbar(plt_data)375    #             c_bar.set_label(r'$\mathrm{P}_\mathrm{x}(x)$')376    #377    #         elif self._size == 3:378    #             if ax is None:379    #                 _, ax = plt.subplots(subplot_kw={'projection': '3d'})380    #                 ax.view_init(35, 45)381    #                 ax.set(xlabel='$x_1$', ylabel='$x_2$', zlabel='$x_3$')382    #383    #             plt_data = ax.scatter(x[:, 0], x[:, 1], x[:, 2], s=15, c=pmf_plt)384    #385    #             c_bar = plt.colorbar(plt_data)386    #             c_bar.set_label(r'$\mathrm{P}_\mathrm{x}(x)$')387    #388    #         return plt_data389    #390    #     else:391    #         raise NotImplementedError('Plot method only supported for 2- and 3-dimensional data.')392# rng = np.random.default_rng()393# n = 10394# m = np.random.random((1, 3))395# m = m / m.sum()396# d = Empirical(n, m, rng)397# d.plot_pmf()398# d.mean399# d.mode400# d.cov401# d.rvs()402# d.pmf(d.rvs())403# d.pmf(d.rvs(4).reshape((2, 2) + d.mean.shape))404class DirichletEmpiricalRV(DiscreteRV):405    """406    Dirichlet-Empirical random process, finite-supp realizations.407    """408    def __init__(self, n, alpha_0, mean, rng=None):409        super().__init__(rng)410        self._n = _empirical_check_n(n)411        self._alpha_0 = _dirichlet_check_alpha_0(alpha_0)412        self._mean = _check_func_pmf(mean, full_support=False)413        self._update_attr()414    # Input properties415    @property416    def n(self):417        return self._n418    @n.setter419    def n(self, n):420        self._n = _empirical_check_n(n)421        self._update_attr()422    @property423    def alpha_0(self):424        return self._alpha_0425    @alpha_0.setter426    def alpha_0(self, alpha_0):427        self._alpha_0 = _dirichlet_check_alpha_0(alpha_0)428        self._update_attr()429    @property430    def mean(self):431        return self._mean432    @mean.setter433    def mean(self, mean):434        self._mean = _check_func_pmf(mean, full_support=True)435        self._update_attr()436    # Attribute Updates437    def _update_attr(self):438        self._data_shape = self._mean.shape439        self._data_size = self._mean.size440        # TODO: mode? cov?441        # self._cov = ((1/self._n + 1/self._alpha_0) / (1 + 1/self._alpha_0)442        #              * (diag_gen(self._mean) - outer_gen(self._mean, self._mean)))443        self._log_pmf_coef = (444            gammaln(self._alpha_0)445            - np.sum(gammaln(self._alpha_0 * self._mean.val))446            + gammaln(self._n + 1)447            - gammaln(self._alpha_0 + self._n)448        )449    def _rvs(self, size=(), random_state=None):450        # return rng.multinomial(self._n, self._mean.flatten(), size).reshape(size + self._shape) / self._n451        raise NotImplementedError452    def pmf(self, x):453        x = _empirical_check_input(x, self._n, self._mean)454        log_pmf = self._log_pmf_coef + (455            gammaln(self._alpha_0 * self._mean.val + self._n * x) - gammaln(self._n * x + 1)456        ).reshape(-1, self._data_size).sum(axis=-1)457        return np.exp(log_pmf)458    # def plot_pmf(self, ax=None):        # TODO: reused code. define simplex plot_xy outside!459    #460    #     if self._size in (2, 3):461    #         x = simplex_grid(self.n, self._shape)462    #         pmf_plt = self.pmf(x)463    #         x.resize(x.shape[0], self._size)464    #465    #         if self._size == 2:466    #             if ax is None:467    #                 _, ax = plt.subplots()468    #                 ax.set(xlabel='$x_1$', ylabel='$x_2$')469    #470    #             plt_data = ax.scatter(x[:, 0], x[:, 1], s=15, c=pmf_plt)471    #472    #             c_bar = plt.colorbar(plt_data)473    #             c_bar.set_label(r'$\mathrm{P}_\mathrm{x}(x)$')474    #475    #         elif self._size == 3:476    #             if ax is None:477    #                 _, ax = plt.subplots(subplot_kw={'projection': '3d'})478    #                 ax.view_init(35, 45)479    #                 ax.set(xlabel='$x_1$', ylabel='$x_2$', zlabel='$x_3$')480    #481    #             plt_data = ax.scatter(x[:, 0], x[:, 1], x[:, 2], s=15, c=pmf_plt)482    #483    #             c_bar = plt.colorbar(plt_data)484    #             c_bar.set_label(r'$\mathrm{P}_\mathrm{x}(x)$')485    #486    #         return plt_data487    #488    #     else:489    #         raise NotImplementedError('Plot method only supported for 2- and 3-dimensional data.')490# rng = np.random.default_rng()491# n = 10492# a0 = 600493# m = np.ones((1, 3))494# m = m / m.sum()495# d = DirichletEmpirical(n, a0, m, rng)496# d.plot_pmf()497# d.mean498# d.mode499# d.cov500# d.rvs()501# d.pmf(d.rvs())502# d.pmf(d.rvs(4).reshape((2, 2) + d.mean.shape))503class EmpiricalRP(DiscreteRV):  # CONTINUOUS504    """505    Empirical random process, continuous support.506    """507    def __init__(self, n, mean, rng=None):508        super().__init__(rng)509        self._n = _empirical_check_n(n)510        if not isinstance(mean, BaseRE):511            raise TypeError("Mean input must be an RE object.")512        self._mean = mean513        self._update_attr()514    # Input properties515    @property516    def n(self):517        return self._n518    @n.setter519    def n(self, n):520        self._n = _empirical_check_n(n)521        self._update_attr()522    @property523    def mean(self):524        return self._mean525    @mean.setter526    def mean(self, mean):527        if not isinstance(mean, BaseRE):528            raise TypeError("Mean input must be an RE object.")529        self._mean = mean530        self._update_attr()531    # Attribute Updates532    def _update_attr(self):533        self._data_shape = self._mean.data_shape534        # self._size = self._mean.size535        # self._mode = ((self._n * self._mean) // 1) + FiniteDomainFunc(self._mean.supp,536        #                                                               simplex_round((self._n * self._mean.val) % 1))537        # TODO: IMPLEMENT COV538        # self._cov = (diag_gen(self._mean) - outer_gen(self._mean, self._mean)) / self._n539        # self._log_pmf_coef = gammaln(self._n + 1)540    def _rvs(self, size=(), random_state=None):541        raise NotImplementedError  # FIXME542        vals = random_state.multinomial(self._n, self._mean.val.flatten(), size).reshape(543            size + self._shape544        )545        if size == ():546            return FiniteDomainFunc(self.mean.supp, vals)547        else:548            return [FiniteDomainFunc(self.mean.supp, val) for val in vals]549    # def pmf(self, x):550    #     x = _empirical_check_input(x, self._n, self._mean)551    #552    #     log_pmf = self._log_pmf_coef + (xlogy(self._n * x.val, self._mean.val)553    #                                     - gammaln(self._n * x.val + 1)).reshape(-1, self._size).sum(axis=-1)554    #     return np.exp(log_pmf)555class SampsDE(BaseRE):556    """557    FAKE samples from continuous DP realization558    """559    def __init__(self, n, alpha_0, mean, rng=None):560        super().__init__(rng)561        self._n = _empirical_check_n(n)562        self._alpha_0 = _dirichlet_check_alpha_0(alpha_0)563        if not isinstance(mean, BaseRE):564            raise TypeError("Mean input must be an RE object.")565        self._mean = mean566        self._data_shape = mean.data_shape567    # Input properties568    @property569    def n(self):570        return self._n571    @property572    def alpha_0(self):573        return self._alpha_0574    @property575    def mean(self):576        return self._mean577    def _rvs(self, size=(), random_state=None):578        if size != ():579            raise ValueError("Size input not used, 'n' is.")580        emp = []581        for n in range(self.n):582            p_mean = 1 / (1 + n / self.alpha_0)583            if random_state.choice([True, False], p=[p_mean, 1 - p_mean]):584                # Sample from mean dist585                emp.append([self.mean.rvs(), 1])586            else:587                # Sample from empirical dist588                cnts = [s[1] for s in emp]589                probs = np.array(cnts) / sum(cnts)590                i = random_state.choice(range(len(emp)), p=probs)591                emp[i][1] += 1592        out = [np.broadcast_to(s, (c, *self.data_shape)) for s, c in emp]593        return np.concatenate(out)594# s, p = ['a','b','c'], np.array([.3,.2,.5])595# p = p / p.sum()596# m = FiniteRE.gen_func(s, p)597# dd = SampsDE(10, 5, m)598# print(dd.rvs())599class BetaRV(ContinuousRV):600    """601    Beta random variable.602    """603    def __init__(self, a, b, rng=None):604        super().__init__(rng)605        if a <= 0 or b <= 0:606            raise ValueError("Parameters must be strictly positive.")607        self._a, self._b = a, b608        self._data_shape = ()609        self._update_attr()610    # Input properties611    @property612    def a(self):613        return self._a614    @a.setter615    def a(self, a):616        if a <= 0:617            raise ValueError618        self._a = a619        self._update_attr()620    @property621    def b(self):622        return self._b623    @b.setter624    def b(self, b):625        if b <= 0:626            raise ValueError627        self._b = b628        self._update_attr()629    # Attribute Updates630    def _update_attr(self):631        if self._a > 1:632            if self._b > 1:633                self._mode = (self._a - 1) / (self._a + self._b - 2)634            else:635                self._mode = 1636        elif self._a <= 1:637            if self._b > 1:638                self._mode = 0639            elif self._a == 1 and self._b == 1:640                self._mode = 0  # any in unit interval641            else:642                self._mode = 0  # any in {0,1}643        self._mean = self._a / (self._a + self._b)644        self._cov = self._a * self._b / (self._a + self._b) ** 2 / (self._a + self._b + 1)645    def _rvs(self, size=(), random_state=None):646        return random_state.beta(self._a, self._b, size)647    def _pdf(self, x):648        log_pdf = xlog1py(self._b - 1.0, -x) + xlogy(self._a - 1.0, x) - betaln(self._a, self._b)649        return np.exp(log_pdf)650    def plot_pdf(self, n_plt, ax=None):651        if ax is None:652            _, ax = plt.subplots()653            ax.set(xlabel="$x$", ylabel="$P_{\mathrm{x}}(x)$")654        x_plt = np.linspace(0, 1, n_plt + 1, endpoint=True)655        plt_data = ax.plot(x_plt, self.pdf(x_plt))...

columns_config.py

Source:columns_config.py

1columns_config = {2    'qs1':{3        'conversion':{4            'values':{5                '_NaN':'_mean'6            },7         },8        'continuous': True,9    },10    'qs2':{11        'conversion': {12            'values':{13                '_NaN':9414            }15        },16        'one_hot_encoding': True,17    },18    'qs3':{19        'conversion': {20            'values':{21                '_NaN':9422            }23        },24        'one_hot_encoding': True,25    },26    'NORTHEAST':{27        'conversion': {28            'values':{29                '_NaN':030            }31        },32    },33    'MIDWEST':{34        'conversion': {35            'values': {36                '_NaN':037            }38        },39    },40    'SOUTH':{41        'conversion': {42            'values': {43                '_NaN':044            }45        },46    },47    'WEST':{48        'conversion': {49            'values': {50                '_NaN':051            }52        },53    },54    'Q1VALUE':{55        'conversion': {56            'values': {57                9998:'_mean',58                9999:'_mean'59            },60        },61        'continuous': True,62    },63    'Q2VALUE':{64        'conversion': {65            'values': {66                9998:'_mean',67                9999:'_mean'68            },69        },70        'continuous': True,71    },72    'Q2Q1DIF':{73        'conversion': {74            'values': {75                '_NaN':'_mean',76                9998:'_mean',77                9999:'_mean'78            },79        },80        'continuous': True81    },82    'Q3VALUE':{83        'conversion': {84            'values': {85                9998:'_mean',86                9999:'_mean'87            },88        },89        'continuous': True,90    },91    'Q4VALUE':{92        'conversion': {93            'values': {94                9998:'_mean',95                9999:'_mean'96            },97        },98        'continuous': True,99    },100    'Q4Q3DIF':{101        'conversion': {102            'values': {103                '_NaN':'_mean',104                9998:'_mean',105                9999:'_mean'106            },107        },108        'continuous': True,109        'available_types':['int','float','float64']110    },111    'Q3Q1DIF':{112        'conversion': {113        },114        'continuous': True,115    },116    'Q4Q2DIF':{117        'conversion': {118        },119        'continuous': True,120    },121    'Q4Q2DIFQ3Q1DIFTOTAL':{122        'conversion': {123        },124        'continuous': True,125    },126    'Q5':{127        'conversion': {128            'values': {129                '_NaN':'_mean'130            },131        },132        'continuous': True,133    },134    'Q6':{135        'conversion': {136            'values': {137                '_NaN':'_mean'138            },139        },140        'continuous': True141    },142    'Q6Q5DIF':{143        'conversion': {144            'values': {145                '_NaN': '_mean',146            },147        },148        'continuous': True149    },150    'q7':{151        'conversion': {152            'values': {153                '_NaN':94154            }155        },156        'one_hot_encoding': True,157    },158    'q8':{159        'conversion': {160            'values': {161                '_NaN':94162            },163            'astype': 'int64'164        },165        'one_hot_encoding': True,166    },167    'q9':{168        'conversion': {169            'values': {170                '_NaN':94171            }172        },173        'one_hot_encoding': True,174    },175    'q10':{176        'conversion': {177            'values': {178                '_NaN':94179            },180            'astype': 'int64'181        },182        'one_hot_encoding': True183    },184    'q11':{185        'conversion': {186            'values': {187                '_NaN':94188            }189        },190        'one_hot_encoding': True191    },192    'q12':{193        'conversion': {194            'values': {195                '_NaN':94196            }197        },198        'one_hot_encoding': True199    },200    'q13a':{201        'conversion': {202            'values': {203                '_NaN':94204            }205        },206        'one_hot_encoding': True207    },208    'q13b':{209        'conversion': {210            'values': {211                '_NaN':94212            }213        },214        'one_hot_encoding': True215    },216    'q13c':{217        'conversion': {218            'values': {219                '_NaN':94220            }221        },222        'one_hot_encoding': True223    },224    'q13d':{225        'conversion': {226            'values': {227                '_NaN':94228            }229        },230        'one_hot_encoding': True231    },232    'q13e':{233        'conversion': {234            'values': {235                '_NaN':94236            }237        },238        'one_hot_encoding': True239    },240    'q13f':{241        'conversion': {242            'values': {243                '_NaN':94244            }245        },246        'one_hot_encoding': True247    },248    'q13g':{249        'conversion': {250            'values': {251                '_NaN':94252            }253        },254        'one_hot_encoding': True255    },256    'EPWORTH':{257        'conversion': {258            'values': {259                '_NaN':'_mean'260            },261        },262        'continuous': True,263    },264    'q14':{265        'conversion': {266            'values': {267                '_NaN':94268            }269        },270        'one_hot_encoding': True271    },272    'q15':{273        'conversion': {274            'values': {275                '_NaN':94276            }277        },278        'one_hot_encoding': True279    },280    'q16a':{281        'conversion': {282            'values': {283                '_NaN':94284            }285        },286        'one_hot_encoding': True287    },288    'q16c':{289        'conversion': {290            'values': {291                '_NaN':94292            }293        },294        'one_hot_encoding': True295    },296    'q16d':{297        'conversion': {298            'values': {299                '_NaN':94300            }301        },302        'one_hot_encoding': True303    },304    'q16e':{305        'conversion': {306            'values': {307                '_NaN':94308            }309        },310        'one_hot_encoding': True311    },312    'q16f':{313        'conversion': {314            'values': {315                '_NaN':94316            }317        },318        'one_hot_encoding': True319    },320    'q17':{321        'conversion': {322            'values': {323                '_NaN':94324            }325        },326        'one_hot_encoding': True327    },328    'q18':{329        'conversion': {330            'values': {331                '_NaN':94332            }333        },334        'one_hot_encoding': True335    },336    'q19a':{337        'conversion': {338            'values': {339                '_NaN':94340            }341        },342        'one_hot_encoding': True343    },344    'q19b':{345        'conversion': {346            'values': {347                '_NaN':94348            }349        },350        'one_hot_encoding': True351    },352    'q19c':{353        'conversion': {354            'values': {355                '_NaN':94356            }357        },358        'one_hot_encoding': True359    },360    'q19d':{361        'conversion': {362            'values': {363                '_NaN':94364            }365        },366        'one_hot_encoding': True367    },368    'q20':{369        'conversion': {370            'values': {371                '_NaN':94372            }373        },374        'one_hot_encoding': True375    },376    'q21':{377        'conversion': {378            'values': {379                '_NaN':94380            }381        },382        'one_hot_encoding': True383    },384    'q22':{385        'conversion': {386            'values': {387                '_NaN':94388            }389        },390        'one_hot_encoding': True391    },392    'q23':{393        'conversion': {394            'values': {395                '_NaN':94396            }397        },398        'one_hot_encoding': True399    },400    'q24':{401        'conversion': {402            'values': {403                '_NaN':94404            }405        },406        'one_hot_encoding': True407    },408    'q25':{409        'conversion': {410            'values': {411                '_NaN':94412            }413        },414        'one_hot_encoding': True415    },416    'q26':{417        'conversion': {418            'values': {419                '_NaN':94420            }421        },422        'one_hot_encoding': True423    },424    'q27':{425        'conversion': {426            'values': {427                '_NaN':94428            },429            'astype': 'int64'430        },431        'one_hot_encoding': True432    },433    'q28':{434        'conversion': {435            'values': {436                '_NaN':94437            },438            'astype': 'int64'439        },440        'one_hot_encoding': True441    },442    'q29a':{443        'conversion': {444            'values': {445                '_NaN':'_mean',446                98:'_mean',447                99:'_mean',448                97: 0.5449            },450            'astype': 'int64'451        },452        'continuous': True,453    },454    'q29b':{455        'conversion': {456            'values': {457                '_NaN':'_mean',458                98:'_mean',459                99:'_mean',460                97: 0.5461            },462            'astype': 'int64'463        },464        'continuous': True,465    },466    'q29c':{467        'conversion': {468            'values': {469                '_NaN':'_mean',470                98:'_mean',471                99:'_mean',472                97: 0.5473            },474            'astype': 'int64'475        },476        'continuous': True,477    },478    'Q29TOTAL':{479        'conversion': {480            'values': {481                98:'_mean',482                99:'_mean'483            }484        },485        'continuous': True,486    },487    'q30':{488        'conversion': {489            'values': {490                1:4,491                2:3,492                3:2,493                4:1494            }495        },496        'is_target_variable': True497    },498    'q31':{499        'conversion': {500            'values': {501                '_NaN':94502            }503        },504        'one_hot_encoding': True505    },506    'q32':{507        'conversion': {508            'values': {509                '_NaN':94510            }511        },512        'one_hot_encoding': True513    },514    'q33':{515        'conversion': {516            'values': {517                '_NaN':94518            }519        },520        'one_hot_encoding': True521    },522    'q34':{523        'conversion': {524            'values': {525                '_NaN':94526            }527        },528        'one_hot_encoding': True529    },530    'q35':{531        'conversion': {532            'values': {533                '_NaN':'_mean',534                996:'_mean',535                998:'_mean',536                999:'_mean'537            },538        },539        'continuous': True,540        'available_types':['int','float','float64']541    },542    'Q36':{543        'conversion': {544            'values': {545                98:'_mean',546                99:'_mean',547                '_NaN':'_mean'548            },549        },550        'continuous': True,551    },552    'q3701':{553        'conversion': {554            'values': {555                '_NaN':94556            },557            'astype': 'int64'558        },559        'one_hot_encoding': True560    },561    'q3702':{562        'conversion': {563            'values': {564                '_NaN':94565            },566            'astype': 'int64'567        },568        'one_hot_encoding': True569    },570    'q3703':{571        'conversion': {572            'values': {573                '_NaN':94574            },575            'astype': 'int64'576        },577        'one_hot_encoding': True578    },579    'Q38':{580        'conversion': {581            'values': {582                98: '_mean',583                99: '_mean',584                '_NaN': '_mean'585            },586        },587        'continuous': True,588    },589    'q3901': {590        'conversion': {591            'values': {592                '_NaN': 94593            },594            'astype': 'int64'595        },596        'one_hot_encoding': True597    },598    'q3902': {599        'conversion': {600            'values': {601                '_NaN': 94602            },603            'astype': 'int64'604        },605        'one_hot_encoding': True606    },607    'q3903': {608        'conversion': {609            'values': {610                '_NaN': 94611            },612            'astype': 'int64'613        },614        'one_hot_encoding': True615    },616    'Q40':{617        'conversion': {618            'values': {619                98:'_mean',620                99:'_mean',621                '_NaN': '_mean'622            },623        },624        'continuous': True,625    },626    'q4101':{627        'conversion': {628            'values': {629                '_NaN':94630            },631            'astype': 'int64'632        },633        'one_hot_encoding': True634    },635    'q4102':{636        'conversion': {637            'values': {638                '_NaN':94639            },640            'astype': 'int64'641        },642        'one_hot_encoding': True643    },644    'q4103':{645        'conversion': {646            'values': {647                '_NaN':94648            },649            'astype': 'int64'650        },651        'one_hot_encoding': True652    },653    'Q36Q38Q40TOTAL':{654    },655    'Q42':{656        'conversion': {657            'values': {658                98: '_mean',659                99: '_mean',660                '_NaN': '_mean'661            },662        },663        'continuous': True,664    },665    'Q43A':{666        'conversion': {667            'values': {668                98: '_mean',669                99: '_mean',670                '_NaN': '_mean'671            },672        },673        'continuous': True,674    },675    'Q43B':{676        'conversion': {677            'values': {678                98: '_mean',679                99: '_mean',680                '_NaN': '_mean'681            },682        },683        'continuous': True,684    },685    'Q43C':{686        'conversion': {687            'values': {688                98: '_mean',689                99: '_mean',690                '_NaN': '_mean'691            },692        },693        'continuous': True,694    },695    'Q43D':{696        'conversion': {697            'values': {698                98: '_mean',699                99: '_mean',700                '_NaN': '_mean'701            },702        },703        'continuous': True,704    },705    'Q43E': {706        'conversion': {707            'values': {708                98: '_mean',709                99: '_mean',710                '_NaN': '_mean'711            },712        },713        'continuous': True,714    },715    'Q43F': {716        'conversion': {717            'values': {718                98: '_mean',719                99: '_mean',720                '_NaN': '_mean'721            },722        },723        'continuous': True,724    },725    'Q43G1': {726        'conversion': {727            'values': {728                98: '_mean',729                99: '_mean',730                '_NaN': '_mean'731            },732        },733        'continuous': True,734        'available_types':['int','float','float64']735    },736    'Q43G2': {737        'conversion': {738            'values': {739                98: '_mean',740                99: '_mean',741                '_NaN': '_mean'742            },743        },744        'continuous': True,745    },746    'Q43G3': {747        'conversion': {748            'values': {749                98: '_mean',750                99: '_mean',751                '_NaN': '_mean'752            },753        },754        'continuous': True,755    },756    'Q43TOTAL':{757        'conversion': {758            'values': {759                98: '_mean',760                99: '_mean',761                '_NaN': '_mean'762            },763        },764        'continuous': True,765    },766    'q4401': {767        'conversion': {768            'values': {769                '_NaN': 94770            },771            'astype': 'int64'772        },773        'one_hot_encoding': True774    },775    'q4402': {776        'conversion': {777            'values': {778                '_NaN': 94779            },780            'astype': 'int64'781        },782        'one_hot_encoding': True783    },784    'q4403': {785        'conversion': {786            'values': {787                '_NaN': 94788            },789            'astype': 'int64'790        },791        'one_hot_encoding': True792    },793    'q45': {794        'conversion': {795            'values': {796                '_NaN': 94797            }798        },799        'one_hot_encoding': True800    },801    'q46': {802        'conversion': {803            'values': {804                '_NaN': 94805            }806        },807        'one_hot_encoding': True808    },809    'q47': {810        'conversion': {811            'values': {812                '_NaN': 94813            }814        },815        'one_hot_encoding': True816    },817    'q48': {818        'conversion': {819            'values': {820                '_NaN': 94821            }822        },823        'one_hot_encoding': True824    },825    'q49': {826        'conversion': {827            'values': {828                '_NaN': 94829            }830        },831        'one_hot_encoding': True832    },833    'q50': {834        'conversion': {835            'values': {836                '_NaN': 94837            }838        },839        'one_hot_encoding': True840    },841    'q51': {842        'conversion': {843            '_NaN': 94844        },845        'one_hot_encoding': True846    },847    'q53': {848        'conversion': {849            'values': {850                '_NaN': 94851            }852        },853        'one_hot_encoding': True854    },855    'q54': {856        'conversion': {857            'values': {858                '_NaN': 94859            }860        },861        'one_hot_encoding': True862    },863    'q55': {864        'conversion': {865            'values': {866                '_NaN': 94867            }868        },869        'one_hot_encoding': True870    },871    'q56': {872        'conversion': {873            'values': {874                '_NaN': 94875            }876        },877        'one_hot_encoding': True878    },879    'q5701': {880        'conversion': {881            'values': {882                '_NaN': 94,883                9 : 94884            },885            'astype': 'int64'886        },887        'one_hot_encoding': True888    },889    'q5702': {890        'conversion': {891            'values': {892                '_NaN': 94893            },894            'astype': 'int64'895        },896        'one_hot_encoding': True897    },898    'q5703': {899        'conversion': {900            'values': {901                '_NaN': 94902            },903            'astype': 'int64'904        },905        'one_hot_encoding': True906    },907    'q5704': {908        'conversion': {909            'values': {910                '_NaN': 94911            }912        },913    },914    'q58': {915        'conversion': {916            'values': {917                '_NaN': 94918            }919        },920    },921    'SHEEWORK': {922        'conversion': {923            'values': {924                '_NaN': '_mean'925            }926        },927        'continuous': True,928    },929    'SHEEFAMILY': {930        'conversion': {931            'values': {932                '_NaN': '_mean'933            }934        },935        'continuous': True,936    },937    'SHEESOCIAL': {938        'conversion': {939            'values': {940                '_NaN': '_mean'941            }942        },943        'continuous': True,944    },945    'SHEEMOOD': {946        'conversion': {947            'values': {948                '_NaN': '_mean'949            }950        },951        'continuous': True,952    },953    'SHEESEX': {954        'conversion': {955            'values': {956                '_NaN': '_mean'957            }958        },959        'continuous': True,960    },961    'SHEETOTAL': {962        'conversion': {963            'values': {964                '_NaN': '_mean'965            },966        },967        'continuous': True,968    },969    'NSFDISABLE': {970        'conversion': {971            'values': {972                '_NaN': '_mean'973            },974        },975        'continuous': True,976    },977    'WEIGHT': {978        'conversion': {979            'values': {980                '_NaN': '_mean'981            }982        },983        'continuous': True,984    },985    'HEIGHT': {986        'conversion': {987            'values': {988                '_NaN': '_mean'989            },990        },991        'continuous': True,992    },993    'BMI': {994        'conversion': {995            'values': {996                '_NaN': '_mean'997            },998        },999        'continuous': True,1000    },1001    'STOPBAG1': {1002        'conversion': {1003            'values': {1004                '_NaN': 01005            }1006        },1007    },1008    'STOPBAG2': {1009        'conversion': {1010            'values': {1011                '_NaN': '_mean'1012            }1013        },1014        'continuous': True,1015    },1016    'IPAQ36': {1017        'conversion': {1018            'values': {1019                '_NaN': '_mean',1020                98: '_mean',1021                99: '_mean'1022            },1023        },1024        'continuous': True,1025    },1026    'IPAQ38': {1027        'conversion': {1028            'values': {1029                '_NaN': '_mean',1030                98: '_mean',1031                99: '_mean'1032            },1033        },1034        'continuous': True,1035    },1036    'IPAQ40': {1037        'conversion': {1038            'values': {1039                '_NaN': '_mean',1040                98: '_mean',1041                99: '_mean',1042            },1043        },1044        'continuous': True,1045    },1046    'IPAQTOTAL': {1047        'conversion': {1048            'values': {1049                '_NaN': 94,1050                98: '_mean',1051                99: '_mean'1052            },1053        },1054        'continuous': True,1055    }...

normal.py

Source:normal.py

1import numpy as np2import paddle3import paddle.fluid as fluid4from .base import Distribution5__all__ = [6    'Normal',7]8class Normal(Distribution):9    def __init__(self,10                 dtype='float32',11                 param_dtype='float32',12                 is_continues=True,13                 is_reparameterized=True,14                 group_ndims=0,15                 **kwargs):16        super(Normal, self).__init__(dtype, 17                             param_dtype, 18                             is_continues,19                             is_reparameterized,20                             group_ndims=group_ndims,21                             **kwargs)22        try:23            self._std = paddle.cast(paddle.to_tensor([kwargs['std']]), self.dtype) \24                if type(kwargs['std']) in [type(1.), type(1)] else kwargs['std']25            self._logstd = paddle.log(self._std)26        except:27            self._logstd = paddle.cast(paddle.to_tensor([kwargs['logstd']]), self.dtype) \28                if type(kwargs['logstd']) in [type(1.), type(1)] else kwargs['logstd']29            self._std = paddle.exp(self._logstd)30        self._mean = kwargs['mean']31    @property32    def mean(self):33        """The mean of the Normal distribution."""34        return self._mean35    @property36    def logstd(self):37        """The log standard deviation of the Normal distribution."""38        try:39            return self._logstd40        except:41            self._logstd = paddle.log(self._std)42            return self._logstd43    @property44    def std(self):45        """The standard deviation of the Normal distribution."""46        return self._std47    def _sample(self, n_samples=1, **kwargs):48        if n_samples > 1:49            _shape = fluid.layers.shape(self._mean)50            _shape = fluid.layers.concat([paddle.to_tensor([n_samples], dtype="int32"), _shape])51            _len = len(self._std.shape)52            _std = paddle.tile(self._std, repeat_times=[n_samples, *_len*[1]])53            _mean = paddle.tile(self._mean, repeat_times=[n_samples, *_len*[1]])54        else:55            _shape = fluid.layers.shape(self._mean)56            _std = self._std + 0.57            _mean = self._mean + 0.58        if self.is_reparameterized:59            epsilon = paddle.normal(name='sample',60                                    shape=_shape,61                                    mean=0.0,62                                    std=1.0)63            sample_ = _mean + _std * epsilon64        else:65            _mean.stop_gradient = True66            _std.stop_gradient = True67            epsilon = paddle.normal(name='sample',68                                    shape=_shape,69                                    mean=0.0,70                                    std=1.0)71            sample_ = _mean + _std * epsilon72            sample_.stop_gradient = False73        self.sample_cache = sample_74        if n_samples > 1:75            assert(sample_.shape[0] == n_samples)76        return sample_77    def _log_prob(self, sample=None):78        if sample is None:79            sample = self.sample_cache80        if len(sample.shape) > len(self._mean.shape):81            n_samples = sample.shape[0]82            _len = len(self._std.shape)83            _std = paddle.tile(self._std, repeat_times=[n_samples, *_len*[1]]) 84            _mean = paddle.tile(self._mean, repeat_times=[n_samples, *_len*[1]]) 85        else:86            _std = self._std87            _mean = self._mean88        ## Log Prob89        if not self.is_reparameterized:90            _mean.stop_gradient = True91            _std.stop_gradient = True92        logstd = paddle.log(_std)93        c = -0.5 * np.log(2 * np.pi)94        precision = paddle.exp(-2 * logstd)95        log_prob = c - logstd - 0.5 * precision * paddle.square(sample - _mean)96        # log_prob = fluid.layers.reduce_sum(log_prob, dim=-1)97        log_prob.stop_gradient = False...

normalize_utils.py

Source:normalize_utils.py

...17            self._mean = self._oldmean + (x-self._mean)/self._n18            self._var = self._var + (x - self._oldmean)*(x - self._mean)19            self._lock.release()20        return np.clip((x-self.mean)/(self.std+1e-5),-self._clipvalue,self._clipvalue)21    def normalize_without_mean(self,x):22        if self._lock.acquire():23            x = np.asarray(x)24            assert x.shape == self._mean.shape25            self._n += 126            self._oldmean = np.array(self._mean)27            self._mean = self._oldmean + (x-self._mean)/self._n28            self._var = self._var + (x - self._oldmean)*(x - self._mean)29            self._lock.release()30        return np.clip((x)/(self.std+1e-5),-self._clipvalue,self._clipvalue)31    @property32    def n(self):33        return self._n34    @property35    def mean(self):36        return self._mean37    @property38    def var(self):39        return self._var if self._n > 1 else np.square(self._mean)40    @property41    def std(self):42        if self.n <= 1:43            return np.sqrt(np.abs(self._mean))44        return np.sqrt(self.var/self.n)45    @property46    def shape(self):47        return self._mean.shape48class Running_Reward_Normalizer(object):49    def __init__(self,shape,lock,clipvalue=5):50        self._lock = lock51        self._clipvalue = clipvalue 52        self._n = 053        self._mean = np.zeros(shape) 54        self._var = np.zeros(shape)55    def store(self,rewards,gamma):56        if self._lock.acquire():57            x = discount_cumsum(rewards,gamma)[0]58            x = np.asarray(x)59            assert x.shape == self._mean.shape60            self._n += 161            self._oldmean = np.array(self._mean)62            self._mean = self._oldmean + (x-self._mean)/self._n63            self._var = self._var + (x - self._oldmean)*(x - self._mean)64            self._lock.release()65    def normalize(self,x):66        if self.n < 1:67            return x68        return np.clip((x-self.mean)/(np.clip(self.std,0,100)+1e-5),-self._clipvalue,self._clipvalue)69    def normalize_without_mean(self,x):70        if self.n < 1:71            return x72        return np.clip((x)/(np.clip(self.std,0,100)+1e-5),-self._clipvalue,self._clipvalue)73    @property74    def n(self):75        return self._n76    @property77    def mean(self):78        return self._mean79    @property80    def var(self):81        return self._var if self._n > 1 else np.square(self._mean)82    @property83    def std(self):...

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