Python preprocessing.PolynomialFeatures类代码示例

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

在下文中一共展示了PolynomialFeatures类的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: init

    def __init__(self):
        self.theta = T.matrix()
        # define output for b
        combinations = PolynomialFeatures._combinations(2, 3, False, False)
        n_output_features_ = sum(1 for _ in combinations) + 1
        self.A_b = theano.shared(
            value=np.ones((n_output_features_,), dtype=theano.config.floatX),
            borrow=True, name='A_b')
        self.b_b = theano.shared(value=1.,
                                 borrow=True, name='b_b')

        combinations = PolynomialFeatures._combinations(2, 3, False, False)
        L = [(self.theta[:, 0] ** 0).reshape([-1, 1])]
        for i, c in enumerate(combinations):
            L.append(self.theta[:, c].prod(1).reshape([-1, 1]))
        self.XF3 = T.concatenate(L, axis=1)
        b = (T.dot(self.XF3, self.A_b) + self.b_b).reshape([-1, 1])

        # define output for k
        combinations = PolynomialFeatures._combinations(2, 2, False, False)
        n_output_features_ = sum(1 for _ in combinations) + 1
        self.rho_k = theano.shared(
            value=np.ones((n_output_features_,), dtype=theano.config.floatX),
            borrow=True, name='rho_k')

        combinations = PolynomialFeatures._combinations(2, 2, False, False)
        L = [(self.theta[:, 0] ** 0).reshape([-1, 1])]
        for i, c in enumerate(combinations):
            L.append(self.theta[:, c].prod(1).reshape([-1, 1]))
        self.XF2 = T.concatenate(L, axis=1)
        k = T.dot(self.XF2, self.rho_k).reshape([-1, 1])

        self.outputs = [T.concatenate([b, k], axis=1)]
        self.inputs = [self.theta]
        self.trainable_weights = [self.A_b, self.b_b, self.rho_k]

开发者ID:teopir，项目名称:ifqi，代码行数:35，代码来源:pbo_grad_test.py

示例2: prob_max

def prob_max(x):
    poly=PolynomialFeatures(degree=2)
    x=poly.fit_transform(x)
    
    ####define best fit coefficient arrays

    theta_0=np.array([5.017034759466216798e+00,-4.953976374628412532e-02,-5.853604893727188709e-03,-1.732076056200582692e-01,4.646876717720006822e-02,-2.787195959859810248e-04,-1.222728739255723981e-07,6.120106921025333935e-02,4.604924515407455714e-06,-1.475861223279032741e-01,-4.060326310707941784e-09,1.177855732870812001e-02,3.113699082333943463e-02,-8.110887996756119586e-12,-1.113811480228766704e-05,-1.501651909640449069e-07,-2.190797370951344465e-06,-1.718990505473245339e-05,-1.199898098055512375e-13,-2.571924773608319866e-07,-2.147269697093823931e-12,-3.256296536440236682e-05,-2.581007347409745425e-05,1.392377894191479523e-03,-4.129157456238496948e-02,-1.811677361205055875e-02,-7.083807139833804416e-06,4.116671309652412958e-02,3.361594896247442773e-04,-8.223201336497298203e-03,-1.862209709284966395e-07,1.527880447451521184e-02,-3.672245027121902317e-02,-4.975817315933817863e-10,-6.237344094335352815e-04,-1.217106713769066128e-05,-1.489610233924158246e-04,-1.156461881655085214e-03,-5.159561821638818347e-12,-1.884192981459143558e-05,-1.825179242529750414e-10,-5.438522396156177874e-07,4.167833399722946711e-05,5.607144654864255374e-03,-3.093787958451529527e-02,-2.041422430639949412e-04,7.895983583095988675e-03,1.293062803926413491e-02,5.899640081165494730e-03,-1.021176015149306061e-05,8.486220614842233598e-03,5.822368958314040610e-03,-2.243937133831174112e-08,-8.464968966797879399e-03,-1.906386791427585779e-04,-1.795243901952780228e-03,-1.046895210502369993e-02,-3.330917120202175767e-10,-4.235251180738666644e-04,-5.694559236692822056e-09,-1.583929993116185621e-03,1.629024063907276165e-01,-6.967989967191325247e-03,-3.673107962032413740e-06,-2.280088579624509337e-01,1.726846693277796316e-04,1.013912471248917396e-01,-7.647706080406362405e-08,-3.240179256710575273e-01,1.214811767523774205e-01,-3.401281050759457049e-10,-1.670938331047893612e-07,-7.369899627351106136e-06,-9.856333774434332797e-05,-4.534506039623955074e-05,-9.599184784444142215e-12,-5.151527253102048208e-06,-1.030689454605035745e-10,4.646876717720006822e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-2.787195959859810248e-04,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.222728739255723981e-07,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,6.120106921025333935e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,4.604924515407455714e-06,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.475861223279032741e-01,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-4.060326310707941784e-09,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,1.177855732870812001e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,3.113699082333943463e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-8.110887996756119586e-12,0.000000000000000000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    theta_1=np.array([-9.861773710361221745e+00,1.930173314618176539e-01,6.178220889126870112e-03,9.590504030349773779e-02,-2.071552578564340164e-01,1.061401114719375260e-01,3.276754675639340086e-02,-1.761411784826558136e-02,-1.219468120911304441e-06,-9.174348236081142360e-02,2.132582599900693932e-02,-1.168137887722866912e-02,1.014151234082172059e-01,9.598987135568204463e-04,-4.542651588690940767e-02,-7.514592183950008714e-04,1.113651743862166532e-03,3.535033504077587929e-02,1.348878960300472899e-06,4.158088521609443200e-02,1.744377835470558925e-06,-8.830070079582454969e-04,-6.118986593694282407e-05,-4.784785490618059583e-04,6.231388236713353984e-02,1.984193321394733464e-02,3.807758267577563555e-02,-1.857758661936751918e-02,-8.902117282652563328e-05,1.684544497032977118e-03,-4.354918224284388961e-02,8.135671785350261087e-03,1.838040795364327554e-03,4.648089395429296639e-02,1.603282923510754299e-02,-5.706248765095311287e-02,6.474737189221846378e-02,-1.666585875194382532e-02,5.800179529291954185e-05,6.960244357250958136e-02,1.482721160150063508e-04,-5.299760763074679222e-07,-4.512899253144341872e-05,-9.330422825892547602e-04,-3.692049341246863322e-04,-7.641113350637301687e-04,-3.553288559473667197e-04,-3.424266483519060756e-03,4.323086081437536800e-04,-4.955185382381825611e-04,-5.468633412309427573e-03,3.023053081335558886e-04,2.032432933463332054e-03,-1.868881428527514009e-04,5.907286677952040300e-03,1.224575926635180362e-03,1.491552037995557810e-03,3.744487993794240379e-03,-1.585824627682363985e-03,4.626090019667926378e-03,2.914276434916693195e-04,-6.421237001048539506e-04,1.343912634023189216e-02,1.202887078507273999e-02,4.579648647433440592e-03,-4.573005453417482836e-05,-2.603037492365091118e-02,1.093608117200833424e-01,3.532167048002045617e-01,-1.790610728587208392e-02,-7.755213616683120925e-02,-5.213887650785711293e-03,-1.747560651202587356e-01,-4.635745132339050972e-02,-5.689835106400319142e-02,1.079103168240419384e-04,8.490464847112829186e-03,8.373013610258914587e-05,-2.071552578564340164e-01,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,1.061401114719375260e-01,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,3.276754675639340086e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.761411784826558136e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.219468120911304441e-06,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-9.174348236081142360e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,2.132582599900693932e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.168137887722866912e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,1.014151234082172059e-01,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,9.598987135568204463e-04,0.000000000000000000e+00,0.00000000000000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    theta_2=np.array([-2.604982997969506187e+01,2.522175474048852784e-01,6.275718741926675920e-03,1.273176496282046599e-01,-1.716361908427019300e-01,-8.312891928874267811e-02,4.068642760504040390e-02,-2.445951924349220458e-02,-8.746331909292573688e-07,-1.542657353435612777e-02,-1.765684782956331370e-02,-3.195224775777173168e-04,2.484350665759416446e-02,4.813993958703906978e-03,1.759866699719525307e-01,5.747258345660388864e-04,-1.022129045161229450e-03,5.567310929387970370e-02,-9.063835339582872293e-07,-8.930479773136143495e-02,-9.138473645722535673e-07,-7.459379939882724523e-04,-4.125238423403655301e-05,-4.278814974555324602e-04,1.234252674940865789e-02,4.708747007997553247e-02,2.657070242802176546e-02,6.926664427951148562e-02,-6.384822293781164664e-05,4.964280033292678418e-02,9.853135356553717472e-02,-2.621681271491586862e-02,6.630289966406467672e-02,-2.208061355155441774e-01,4.922574438806641417e-02,4.310173077725486246e-02,-5.622794820973487512e-02,1.006576646572381883e-01,-3.897449196020566275e-05,-7.080593340274707326e-03,-7.767702598866720021e-05,-3.990070230109308789e-07,-1.651061082255117919e-05,1.537024690049966936e-03,8.005698436542285070e-04,8.994568249232704014e-04,5.470196351385650481e-04,-2.455970000128474082e-03,4.988277998095904915e-04,1.262763556509414152e-03,4.601679612131920685e-03,-1.194497842888761268e-04,2.882224654372331132e-03,5.875401491502118233e-04,-2.458015081252763658e-03,5.859965255224170106e-04,-2.547687917446368093e-04,-2.516120690268733393e-03,2.300462784971263851e-03,-2.423523210845587861e-03,-1.539288004294190964e-04,-1.260645266526524456e-02,-2.136594669075533859e-02,-1.240381092246360673e-02,1.775253607050698845e-02,-3.279874465984122252e-05,1.667948986384345557e-03,-1.177656364439296638e-01,-8.947706286380961412e-04,5.282554584883104691e-03,9.528953029071411673e-02,-1.953324553475337816e-03,1.692159896831275101e-01,6.332910268512657870e-02,-3.059270306265245501e-02,-7.251068271668771679e-05,-2.748819360572268139e-02,-4.386467349947201168e-05,-1.716361908427019300e-01,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-8.312891928874267811e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,4.068642760504040390e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-2.445951924349220458e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-8.746331909292573688e-07,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.542657353435612777e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.765684782956331370e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-3.195224775777173168e-04,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,2.484350665759416446e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,4.813993958703906978e-03,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,1.759866699719525307e-01,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,5.747258345660388864e-04,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.022129045161229450e-03,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,5.567310929387970370e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-9.063835339582872293e-07,0.000000000000000000e+00,0.000000000000000000e+00,-8.930479773136143495e-02,0.000000000000000000e+00,-9.138473645722535673e-07])

    theta_3=np.array([-2.972725322795918140e-02,-2.504227156229747453e-01,-9.722118342779062158e-03,1.229149113213241912e-01,2.039923850853684467e-02,-1.805107341267933943e-02,7.334563069172345476e-03,-6.475321568310828764e-04,-8.474944289249388250e-08,9.714545617984883855e-05,-2.075035998257516458e-07,-1.221820060933139164e-05,-1.714475447964966190e-02,-2.129377838506303893e-03,-1.277321374533818017e-06,-2.380156363764723250e-06,-5.273025783548628525e-06,-1.984111789391731009e-03,-1.335426121119178434e-07,3.339996589013074558e-04,-2.141039464532945376e-07,2.576647414932395786e-03,2.945797215512836505e-06,-1.895000552612198606e-04,-1.462288947682845522e-02,-1.951654268095479733e-02,-1.630737487857820273e-02,-5.655678104343885015e-02,-6.186709331152055607e-06,-2.000570956968860184e-02,-1.460798045208372536e-05,-9.892777618470509349e-04,-6.134943418829629652e-02,5.204243735868804843e-02,-6.976997540713989398e-05,-1.924795146172466416e-04,-3.585644621246710678e-04,-8.751744876445026466e-02,-5.742332320812468485e-06,-2.493414480788682872e-02,-1.819883544853002577e-05,1.673188626427698019e-06,-2.199004526442708865e-05,3.929065891591175703e-03,3.411106034336343351e-03,4.689455918427083009e-03,-1.623583183295337906e-02,-2.379764356421232188e-04,4.563617516957610247e-03,-5.845377676580722996e-04,-5.550332977089329420e-03,5.817926665026248653e-03,3.489254807540806587e-03,-8.968364091790517831e-04,-2.770023159211470760e-03,-4.227833625220314175e-03,1.685174688793472349e-03,-3.707142912226834078e-04,-5.865829701672146956e-03,-5.678036659941369801e-04,8.344188249964974876e-05,-2.863247383273172242e-02,-6.482258485425367728e-03,-4.199374526758931081e-02,-1.256077522453134809e-02,-3.178104108468527999e-06,-3.440396173308768457e-02,-9.901849306080791411e-06,-3.180423753092536477e-05,-7.452030759889874401e-02,-6.907406950607837548e-02,5.971308793973397274e-07,-1.155260382492086013e-04,-2.332571299853613211e-04,1.410515664042338024e-01,-1.068340896895342663e-05,2.499449671921087357e-01,-1.027698942975813230e-05,2.039923850853684467e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.805107341267933943e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,7.334563069172345476e-03,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-6.475321568310828764e-04,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-8.474944289249388250e-08,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,9.714545617984883855e-05,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-2.075035998257516458e-07,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.221820060933139164e-05,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.714475447964966190e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-2.129377838506303893e-03,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.277321374533818017e-06,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-2.380156363764723250e-06,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-5.273025783548628525e-06,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.984111789391731009e-03,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.335426121119178434e-07,0.000000000000000000e+00,0.000000000000000000e+00,3.339996589013074558e-04,0.000000000000000000e+00,-2.141039464532945376e-07,])

#####calculate probabilities
    z_0=np.sum(x*theta_0)
    z_1=np.sum(x*theta_1)
    z_2=np.sum(x*theta_2)
    z_3=np.sum(x*theta_3)
    
    p_0=1./(1.+np.exp(-z_0))
    p_1=1./(1.+np.exp(-z_1))
    p_2=1./(1.+np.exp(-z_2))
    p_3=1./(1.+np.exp(-z_3))

    prob_arra=np.array([p_0, p_1, p_2, p_3])
    
    return prob_arra.argmax()

开发者ID:dsrub，项目名称:web_app，代码行数:28，代码来源:predict_prob.py

示例3: linearRegreSin

def linearRegreSin(url,degree):
    [a,b] = getData(url)
    trainA = a[0:139]
    trainB = b[0:139]
    testA = a[140:]
    testB = b[140:]

    poly = PolynomialFeatures(degree)
    trainA = np.float64(poly.fit_transform(trainA))
    testA = np.float64(poly.fit_transform(testA))
    theta = np.dot(np.dot(np.linalg.inv(np.dot(trainA.T,trainA)),trainA.T),trainB)
    plt.figure(1)
    plt.xlabel('x')
    plt.ylabel('y')
    plt.title('data')
    plt.plot(trainA[:,1],trainB,"r*")
    y=np.dot(trainA, theta)
    print(pow(sum((y-trainB)**2),1/2)/140) #print MSE

    y=np.dot(testA, theta)
    #plt.plot(testA[:,1], testB, "r.")
    plt.plot(testA[:,1],y,"k*")
    print(pow(sum((y-testB)**2),1/2)/60) #print MSE
    plt.show()
    print(theta)

开发者ID:MorPhingG，项目名称:MachineLearning，代码行数:25，代码来源:LinearRegression.py

示例4: myTradingSystem

def myTradingSystem(DATE, OPEN, HIGH, LOW, CLOSE, VOL, OI, P, R, RINFO, exposure, equity, settings):
    """ This system uses linear regression to allocate capital into the desired equities"""

    # Get parameters from setting
    nMarkets = len(settings['markets'])
    lookback = settings['lookback']
    dimension = settings['dimension']
    threshold = settings['threshold']

    pos = np.zeros(nMarkets, dtype=np.float)

    poly = PolynomialFeatures(degree=dimension)
    for market in range(nMarkets):
        reg = linear_model.LinearRegression()
        try:
            reg.fit(poly.fit_transform(np.arange(lookback).reshape(-1, 1)), CLOSE[:, market])
            trend = (reg.predict(poly.fit_transform(np.array([[lookback]]))) - CLOSE[-1, market]) / CLOSE[-1, market]

            if abs(trend[0]) < threshold:
                trend[0] = 0

            pos[market] = np.sign(trend)

        # for NaN data set position to 0
        except ValueError:
            pos[market] = .0

    return pos, settings

开发者ID:marcoracer，项目名称:quantiacs-python，代码行数:28，代码来源:LinearRegression.py

示例5: poly_model

def poly_model(ins,outs,degrees):
	poly   = PolynomialFeatures(degree=degrees)
	X = poly.fit_transform(ins)

	regr = linear_model.LinearRegression()
	regr.fit(X, outs)
	print_model("poly-"+str(degrees), regr, X, outs)

开发者ID:verdverm，项目名称:pypge，代码行数:7，代码来源:theline.py

示例6: interactor

def interactor(df):
    """ This function takes in a data frame and creates binary interaction
    terms from all numerical and categorical variables as well as the assessment
    questions, and outputs a data frame """

    my_data_complete = df.dropna()
    # interactions can only be done for non-missings
    colnames = list(my_data_complete.columns.values)
    # id and date columns
    id_cols_list = [
        x
        for x in colnames  # only for continuous vars
        if not (bool(re.search("_N$", x)) | bool(re.search("_C$", x)) | bool(re.search("_Q$", x)))
    ]
    # actual feature columns - to make interactions from
    new_cols_list = [
        x
        for x in colnames  # only for continuous vars
        if (bool(re.search("_N$", x)) | bool(re.search("_C$", x)) | bool(re.search("_Q$", x)))
    ]
    othervars = my_data_complete[id_cols_list]
    little_df = my_data_complete[new_cols_list]
    # computing all binary interaction terms
    poly = PolynomialFeatures(degree=2, interaction_only=True)
    theints = pd.DataFrame(poly.fit_transform(little_df))
    theints = theints.drop(theints.columns[0], axis=1)  # dropping the first column
    theints.columns = list(new_cols_list + list(itertools.combinations(new_cols_list, 2)))
    # concatenating the interaction terms to the original data frame
    df = pd.DataFrame(othervars.join(theints))
    new_features = theints.columns.values
    return df, new_features

开发者ID:pombredanne，项目名称:babies-public，代码行数:31，代码来源:features.py

示例7: batterLife_chargeMoreThan4

def batterLife_chargeMoreThan4(chargeTime):    
    import numpy as np
    trainDataArr = np.genfromtxt("trainingdata_batteryLife.txt", delimiter = ",")
    trainDataArr = trainDataArr[trainDataArr[ :,0] > 4]
    trainData = trainDataArr[:, 0]
    trainData = trainData.reshape(-1,1)
    trainValue = trainDataArr[:,1]
    testData = np.array(chargeTime)
    testData = testData.reshape(-1,1)
    
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn import linear_model
    
    # Plot outputs
    import matplotlib.pyplot as plt
    plt.scatter(trainData, trainValue,  color='black')
    plt.xticks(())
    plt.yticks(())
    plt.show()

    # Fit regression model
    poly = PolynomialFeatures(degree = 1)
    trainData_ = poly.fit_transform(trainData)
    testData_ = poly.fit_transform(testData)
    
    clf = linear_model.LinearRegression()
    clf.fit(trainData_, trainValue)
    return clf.predict(testData_)

开发者ID:JaneEvans，项目名称:Useful-Python3-Codes，代码行数:28，代码来源:2-level-linear-Regression_batteryLife.py

示例8: hidden_layer

 def hidden_layer(self, X, w):
     # The dimension of matrix Z is (R + 1) * m. The extra dimension is constant
     # extra 1 dimension for bias.
     Z = sigmoid(np.dot(X, w.T))
     p = PolynomialFeatures(degree = 1)
     Z = p.fit_transform(Z)
     return Z

开发者ID:AngeloK，项目名称:cs584-hws，代码行数:7，代码来源:mlp.py

示例9: learning_curve

def learning_curve(classifier, X, y, cv, sample_sizes,
    degree=1, pickle_path=None, verbose=True):
    """ Learning curve
    """

    learning_curves = []
    for i, (train_index, test_index) in enumerate(cv):
        X_train = X[train_index]
        X_test = X[test_index]
        y_train = y[train_index]
        y_test = y[test_index]

        if degree > 1:
            poly = PolynomialFeatures(degree=degree, interaction_only=False, include_bias=True)
            X_train = poly.fit_transform(X_train)
            X_test = poly.transform(X_test)

        lc = []
        for sample in sample_sizes:
            classifier.fit(X_train[:sample], y_train[:sample])

            # apply classifier on test set
            y_pred = classifier.predict(X_test)
            confusion = metrics.confusion_matrix(y_test, y_pred)
            lc.append(balanced_accuracy_expected(confusion))

        learning_curves.append(lc)
        if verbose: print(i, end=' ')
    
    # pickle learning curve
    if pickle_path:
        with open(pickle_path, 'wb') as f:
            pickle.dump(learning_curves, f, protocol=4)
    if verbose: print()

开发者ID:davidjwu，项目名称:mclass-sky，代码行数:34，代码来源:classifier.py

示例10: get_cl

def get_cl(tau, consider='EE', degree=5):
    if consider == 'EE':
        values = values_EE
    else:
        values = values_BB

    v = values#[:100]
    p = points#[:100]

    poly = PolynomialFeatures(degree=degree)
    # Vandermonde matrix of pre-computed paramter values.
    X_ = poly.fit_transform(p.reshape(-1,1))

    predict = np.array([tau]).reshape(1,-1)
    # Creates matrix of values you want to estimate from the existing
    # measurements. Computation speed scales very slowly when you ask for
    # estimate of many sets of parameters.
    predict_ = poly.fit_transform(predict)

    clf = LinearRegression()
    estimate = []
    for l in range(2, v.shape[1]):
        values_l = v[:,l]
        clf.fit(X_, values_l)
        estimate_l = clf.predict(predict_)
        estimate.append(estimate_l)
    estimate = np.array(estimate)

    ell = np.arange(2, l+1)
    Z = 2*np.pi/(ell*(ell+1))
    return ell, Z*estimate[:,0]

开发者ID:pqrs6，项目名称:clee-fast，代码行数:31，代码来源:main_tau.py

示例11: polynomial_expansion

	def polynomial_expansion(self, rank=2): 
		"""
		Expand the features with polynonial of rank rnank 
		"""
		pf = PolynomialFeatures(degree=2)
		self.X_red = pf.fit_transform(self.X_red)
		self.X_white = pf.fit_transform(self.X_white)

开发者ID:Borisdatzar，项目名称:machine_learning_techniques，代码行数:7，代码来源:wine_preprocesser.py

示例12: test_polynomial_fits

def test_polynomial_fits(x, y, n_comps, model, k_folds=3):
  for i in range(1,6):
    poly = PolynomialFeatures(degree=i)
    poly_x = poly.fit_transform(x)
    r2_mean, r2_std, mse_mean, mse_std = run_conventional_linkage(poly_x, y, n_comps, model)
    print r2_mean, r2_std, mse_mean, mse_std
    print

开发者ID:Materials-Informatics-Class-Fall2015，项目名称:MIC-Ternary-Eutectic-Alloy，代码行数:7，代码来源:smart_pipeline.py

示例13: analysis_7

def analysis_7(df_Coredata):
	""" 多次元多項式モデル """

	#https://www.jeremyjordan.me/polynomial-regression/

	X = df_Coredata[['d','e','f','g','i']]
	y = df_Coredata['j']

	# グラフのスタイルを指定
	sns.set(style = 'whitegrid', context = 'notebook')
	# 変数のペアの関係をプロット
	#sns.pairplot(df_Coredata)
	#plt.show()


	#X_train, X_test, y_train, y_test  =  train_test_split(X,y,random_state = 0)
	#lr = linear_model.LinearRegression().fit(X_train, y_train)
	#print("Trainng set score: {:.2f}".format(lr.score(X_train, y_train)))
	#print("Test set score: {:.2f}".format(lr.score(X_test, y_test)))

	### データのスケール変換
	# 標準化
	std_Scaler = StandardScaler()
	data_std = std_Scaler.fit_transform(X)

	mmx_Scaler =MinMaxScaler()
	X_scaled = mmx_Scaler.fit_transform(X)
	#X_test_scaled = scaler.transform(X_test)

	#print(X_train_scaled)

	poly = PolynomialFeatures(degree = 2).fit(data_std)
	print(poly.get_feature_names())

开发者ID:Yotaro723，项目名称:test_2，代码行数:33，代码来源:DoE.py

示例14: main

def main():
    testfile = sys.argv[1]
    modelfile = sys.argv[2]
    polyorder = int(sys.argv[3])
    testweeks = sys.argv[4]

    test_data = np.genfromtxt(testfile, delimiter=',', skip_header=1)

    X = test_data[:,:-1]
    y = test_data[:,-1]

    poly = PolynomialFeatures(degree=polyorder)
    Xpoly = poly.fit_transform(X)

    with open(modelfile, 'rb') as model, open(testweeks) as weeks:
        lr = pickle.load(model)
        games_per_week = (int(line) for line in weeks)
        ranges = []
        pos = 0
        for week in games_per_week:
            newpos = pos + week
            ranges.append( (pos, newpos) )
            pos = newpos
        print('W\tL\tPoints')
        weekly_results = (evaluate_week(week, Xpoly, y, lr) for week in ranges)
        for result in weekly_results:
            print('\t'.join(str(piece) for piece in result))

开发者ID:atmapersaud，项目名称:nfl-predictions，代码行数:27，代码来源:nfl-evaluate.py

示例15: mvr

def mvr(data): 
    x = data[:, 0:len(data[0])-1]
    y = data[:, -1]
    
    minTestingError = np.inf
    for dim in xrange(1,3):
        if(dim > 1):
            print("Mapping into higher dimension of {} \n".format(dim))
        else:
            evaluateGradientDesc(data)
            print("Explicit solution\n")
        poly = PolynomialFeatures(dim)
        z = poly.fit_transform(x)
        
        theta = fitModel(z , y)
        
        print("Intercept     :   {} \nCoefficients : {}\n".format(theta[0], theta[1:]))
        testingError, sol = evaluateModel(z,y, False)
        
        if(dim == 1):
            print "Testing Error :", testingError
        
        if (testingError < minTestingError):
            minTestingError = testingError
            optimalDimension = dim
            optSol = sol
         
    print "Optimal Dimension : {}, Testing Error : {} ".format(optimalDimension, minTestingError)
    return optSol

开发者ID:rakeshadk7，项目名称:MachineLearning，代码行数:29，代码来源:mvr.py

示例16: init_predict

def init_predict(mode):
    """ 整理为用于预测的 X

    i: features
    o: X
    """
    import scipy.io as sio
    import scipy as sp
    from sklearn.preprocessing import PolynomialFeatures

    uid_ave = sio.loadmat('predict_cut_uid_ave.mat')['X']
    poly = PolynomialFeatures(degree=2)
    poly_uid_ave = poly.fit_transform(uid_ave)
    combined_list = [sp.sparse.csc_matrix(poly_uid_ave)]

    if mode == 'f':
        X_words = sio.loadmat('predict_cut_Xf.mat')['X']
    elif mode == 'c':
        X_words = sio.loadmat('predict_cut_Xc.mat')['X']
    else:
        X_words = sio.loadmat('predict_cut_Xl.mat')['X']
    #transformer = TfidfTransformer()
    #X_tfidf = transformer.fit_transform(X_words)

    combined_list.append(X_words)

    X = sp.sparse.hstack(combined_list)

    print(X.shape)
    return X

开发者ID:organization-lab，项目名称:weibo-predict，代码行数:30，代码来源:regressor.py

示例17: predict

    def predict(self, x):
        ## as it is trained on polynominal features, we need to transform x
        poly = PolynomialFeatures(degree=self.degree)
        polynominal_features = poly.fit_transform(x)[0]

        print polynominal_features.reshape
        return self.model.predict(polynominal_features)

开发者ID:tempflip，项目名称:fin_playground，代码行数:7，代码来源:blog_boston2.py

示例18: predict

    def predict(self, X, coefs):
        # first column of Z is time
        # we will replace the other columns with regressed data

        # clean-up from before
        Z = self.X.copy()
        print type(Z), Z.head()
        print type(coefs), coefs.head()

        poly = PolynomialFeatures(degree=self.n)
        for trial_index, (coefficients, x) in enumerate(izip(coefs, Z)):
            print trial_index, coefficients.shape, x.shape
            # reshape required by t
            t = poly.fit_transform((x[:,0]).reshape(-1,1))
            # only regress on data past reference time
            t = t[self.reference_time:]

            z = np.zeros(x.shape)
            # first column is time
            z[:,0] = x[:,0]
            # columns up to reference time are just 0 and were not regressed
            z[:self.reference_time, 1:] = 0
            # columns after reference_time were regressed with coefficients
            print t.shape, z.shape, coefficients.shape
            z[self.reference_time:, 1:] = np.dot(t, coefficients)
            Z.iloc[trial_index] = z
        return Z

开发者ID:fcesco，项目名称:smelling_sepsis，代码行数:27，代码来源:featurizer.py

示例19: main

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Python preprocessing.PolynomialFeatures类代码示例

示例1: __init__

示例2: prob_max

示例3: linearRegreSin

示例4: myTradingSystem

示例5: poly_model

示例6: interactor

示例7: batterLife_chargeMoreThan4

示例8: hidden_layer

示例9: learning_curve

示例10: get_cl

示例11: polynomial_expansion

示例12: test_polynomial_fits

示例13: analysis_7

示例14: main

示例15: mvr

示例16: init_predict

示例17: predict

示例18: predict

示例19: main

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示例1: init