Python numpy.float_power() Examples

The following are 24 code examples of numpy.float_power(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy , or try the search function .
Example #1
Source File: aiplayer.py    From alpha_zero_othello with MIT License 6 votes vote down vote up
def pick_move(self, game, side):
        possible_moves = game.possible_moves(side)
        if len(possible_moves) == 0:
            possible_moves.append((-1,-1))
        monte_prob = self.monte_carlo(game, side)
        
        if self.train:
            self.temp_state.append((self.preprocess_input(game.board, side), np.divide(monte_prob, np.sum(monte_prob))))
        
        monte_prob = np.float_power(monte_prob, 1/self.tau)
        monte_prob = np.divide(monte_prob, np.sum(monte_prob))
        
        r = random()
        for i, move in enumerate(possible_moves):
            r -= monte_prob[Othello.move_id(move)]
            if r <= 0:
                return move
        return possible_moves[-1] 
Example #2
Source File: Estimators.py    From slates_semisynth_expts with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def estimate(self, query, logged_ranking, new_ranking, logged_value):
        exactMatch=numpy.absolute(new_ranking-logged_ranking).sum() == 0
        currentValue=0.0
        if exactMatch:
            numAllowedDocs=self.loggingPolicy.dataset.docsPerQuery[query]
            validDocs=logged_ranking.size
            invPropensity=None
            if self.loggingPolicy.allowRepetitions:
                invPropensity=numpy.float_power(numAllowedDocs, validDocs)
            else:
                invPropensity=numpy.prod(range(numAllowedDocs+1-validDocs, numAllowedDocs+1), dtype=numpy.float64)
                
            currentValue=logged_value*invPropensity

            self.updateRunningAverage(currentValue)
            denominatorDelta=invPropensity-self.runningDenominatorMean
            self.runningDenominatorMean+=denominatorDelta/self.runningSum
        if self.runningDenominatorMean!=0.0:
            return 1.0*self.runningMean/self.runningDenominatorMean
        else:
            return 0.0 
Example #3
Source File: Estimators.py    From slates_semisynth_expts with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def estimate(self, query, logged_ranking, new_ranking, logged_value):
        exactMatch=numpy.absolute(new_ranking-logged_ranking).sum() == 0
        currentValue=0.0
        if exactMatch:
            numAllowedDocs=self.loggingPolicy.dataset.docsPerQuery[query]
            validDocs=logged_ranking.size
            invPropensity=None
            if self.loggingPolicy.allowRepetitions:
                invPropensity=numpy.float_power(numAllowedDocs, validDocs)
            else:
                invPropensity=numpy.prod(range(numAllowedDocs+1-validDocs, numAllowedDocs+1), dtype=numpy.float64)
                
            currentValue=logged_value*invPropensity

        self.updateRunningAverage(currentValue)
        return self.runningMean 
Example #4
Source File: parameters.py    From ViolenceDetection with Apache License 2.0 6 votes vote down vote up
def _draw_samples(self, size, random_state):
        seed = random_state.randint(0, 10**6, 1)[0]
        samples = self.other_param.draw_samples(size, random_state=ia.new_random_state(seed))

        elementwise = self.elementwise and not isinstance(self.val, Deterministic)

        if elementwise:
            exponents = self.val.draw_samples(size, random_state=ia.new_random_state(seed+1))
        else:
            exponents = self.val.draw_sample(random_state=ia.new_random_state(seed+1))

        # without this we get int results in the case of
        # Power(<int>, <stochastic float param>)
        samples, exponents = both_np_float_if_one_is_float(samples, exponents)
        samples_dtype = samples.dtype

        # float_power requires numpy>=1.12
        #result = np.float_power(samples, exponents)
        # TODO why was float32 type here replaced with complex number
        # formulation?
        result = np.power(samples.astype(np.complex), exponents).real
        if result.dtype != samples_dtype:
            result = result.astype(samples_dtype)

        return result 
Example #5
Source File: test_umath.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example #6
Source File: test_umath.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example #7
Source File: test_umath.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example #8
Source File: test_umath.py    From coffeegrindsize with MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example #9
Source File: test_umath.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example #10
Source File: _continuous_distns.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up
def _argcheck(self, h, k):
        condlist = [np.logical_and(h > 0, k > 0),
                    np.logical_and(h > 0, k == 0),
                    np.logical_and(h > 0, k < 0),
                    np.logical_and(h <= 0, k > 0),
                    np.logical_and(h <= 0, k == 0),
                    np.logical_and(h <= 0, k < 0)]

        def f0(h, k):
            return (1.0 - float_power(h, -k))/k

        def f1(h, k):
            return np.log(h)

        def f3(h, k):
            a = np.empty(np.shape(h))
            a[:] = -np.inf
            return a

        def f5(h, k):
            return 1.0/k

        self.a = _lazyselect(condlist,
                             [f0, f1, f0, f3, f3, f5],
                             [h, k],
                             default=np.nan)

        def f0(h, k):
            return 1.0/k

        def f1(h, k):
            a = np.empty(np.shape(h))
            a[:] = np.inf
            return a

        self.b = _lazyselect(condlist,
                             [f0, f1, f1, f0, f1, f1],
                             [h, k],
                             default=np.nan)
        return h == h 
Example #11
Source File: test_umath.py    From mxnet-lambda with Apache License 2.0 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example #12
Source File: test_umath.py    From pySINDy with MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example #13
Source File: saliency_visualization.py    From VSE-C with MIT License 5 votes vote down vote up
def plot_saliency(raw_img, image_var, img_embedding_var, caption_var):
    dis = (caption_var.squeeze() * img_embedding_var.squeeze()).sum()
    dis.backward(retain_graph=True)

    grad = image_var.grad.data.cpu().squeeze().numpy().transpose((1, 2, 0))
    grad = normalize_grad(grad, stat=True)
    grad = imresize((grad * 255).astype('uint8'), (raw_img.height, raw_img.width)) / 255
    grad = normalize_grad(grad.mean(axis=-1, keepdims=True).repeat(3, axis=-1))
    grad = np.float_power(grad, args.grad_power)

    np_img = np.array(raw_img)
    masked_img = np_img * grad
    final = np.hstack([np_img, masked_img.astype('uint8'), (grad * 255).astype('uint8')])
    return Image.fromarray(final.astype('uint8')) 
Example #14
Source File: parameter.py    From DL.EyeSight with GNU General Public License v3.0 5 votes vote down vote up
def _draw_samples(self, size, random_state):
        seed = random_state.randint(0, 10**6, 1)[0]
        samples = self.other_param.draw_samples(
            size,
            random_state=eu.new_random_state(seed))

        elementwise = self.elementwise and not isinstance(self.val, Deterministic)

        if elementwise:
            exponents = self.val.draw_samples(
                size,
                random_state=eu.new_random_state(seed+1))
        else:
            exponents = self.val.draw_sample(
                random_state=eu.new_random_state(seed+1))

        # without this we get int results in the case of
        # Power(<int>, <stochastic float param>)
        samples, exponents = both_np_float_if_one_is_float(samples, exponents)
        samples_dtype = samples.dtype

        # float_power requires numpy>=1.12
        #result = np.float_power(samples, exponents)
        # TODO why was float32 type here replaced with complex number
        # formulation?
        result = np.power(samples.astype(np.complex), exponents).real
        if result.dtype != samples_dtype:
            result = result.astype(samples_dtype)

        return result 
Example #15
Source File: parameters.py    From imgaug with MIT License 5 votes vote down vote up
def _draw_samples(self, size, random_state):
        rngs = random_state.duplicate(2)
        samples = self.other_param.draw_samples(size, random_state=rngs[0])

        elementwise = (
            self.elementwise
            and not isinstance(self.val, Deterministic))

        if elementwise:
            exponents = self.val.draw_samples(size, random_state=rngs[1])
        else:
            exponents = self.val.draw_sample(random_state=rngs[1])

        # without this we get int results in the case of
        # Power(<int>, <stochastic float param>)
        samples, exponents = both_np_float_if_one_is_float(samples, exponents)
        samples_dtype = samples.dtype

        # TODO switch to this as numpy>=1.15 is now a requirement
        #      float_power requires numpy>=1.12
        # result = np.float_power(samples, exponents)
        # TODO why was float32 type here replaced with complex number
        #      formulation?
        result = np.power(samples.astype(np.complex), exponents).real
        if result.dtype != samples_dtype:
            result = result.astype(samples_dtype)

        return result 
Example #16
Source File: _continuous_distns.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up
def _argcheck(self, h, k):
        condlist = [np.logical_and(h > 0, k > 0),
                    np.logical_and(h > 0, k == 0),
                    np.logical_and(h > 0, k < 0),
                    np.logical_and(h <= 0, k > 0),
                    np.logical_and(h <= 0, k == 0),
                    np.logical_and(h <= 0, k < 0)]

        def f0(h, k):
            return (1.0 - float_power(h, -k))/k

        def f1(h, k):
            return np.log(h)

        def f3(h, k):
            a = np.empty(np.shape(h))
            a[:] = -np.inf
            return a

        def f5(h, k):
            return 1.0/k

        self.a = _lazyselect(condlist,
                             [f0, f1, f0, f3, f3, f5],
                             [h, k],
                             default=np.nan)

        def f0(h, k):
            return 1.0/k

        def f1(h, k):
            a = np.empty(np.shape(h))
            a[:] = np.inf
            return a

        self.b = _lazyselect(condlist,
                             [f0, f1, f1, f0, f1, f1],
                             [h, k],
                             default=np.nan)
        return h == h 
Example #17
Source File: test_umath.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example #18
Source File: math_ops.py    From trax with Apache License 2.0 5 votes vote down vote up
def float_power(x1, x2):
  return power(x1, x2) 
Example #19
Source File: test_umath.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example #20
Source File: test_umath.py    From vnpy_crypto with MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example #21
Source File: _continuous_distns.py    From lambda-packs with MIT License 5 votes vote down vote up
def _argcheck(self, h, k):
        condlist = [np.logical_and(h > 0, k > 0),
                    np.logical_and(h > 0, k == 0),
                    np.logical_and(h > 0, k < 0),
                    np.logical_and(h <= 0, k > 0),
                    np.logical_and(h <= 0, k == 0),
                    np.logical_and(h <= 0, k < 0)]

        def f0(h, k):
            return (1.0 - float_power(h, -k))/k

        def f1(h, k):
            return np.log(h)

        def f3(h, k):
            a = np.empty(np.shape(h))
            a[:] = -np.inf
            return a

        def f5(h, k):
            return 1.0/k

        self.a = _lazyselect(condlist,
                             [f0, f1, f0, f3, f3, f5],
                             [h, k],
                             default=np.nan)

        def f0(h, k):
            return 1.0/k

        def f1(h, k):
            a = np.empty(np.shape(h))
            a[:] = np.inf
            return a

        self.b = _lazyselect(condlist,
                             [f0, f1, f1, f0, f1, f1],
                             [h, k],
                             default=np.nan)
        return h == h 
Example #22
Source File: test_umath.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example #23
Source File: fractal_dfa.py    From NeuroKit with MIT License 5 votes vote down vote up
def _fractal_mfdfa_q(q=2):
    # TODO: Add log calculator for q ≈ 0

    # Fractal powers as floats
    q = np.asarray_chkfinite(q, dtype=np.float)

    # Ensure q≈0 is removed, since it does not converge. Limit set at |q| < 0.1
    q = q[(q < -0.1) + (q > 0.1)]

    # Reshape q to perform np.float_power
    q = q.reshape(-1, 1)
    return q 
Example #24
Source File: fractal_dfa.py    From NeuroKit with MIT License 5 votes vote down vote up
def _fractal_dfa_fluctuation(segments, trends, multifractal=False, q=2):

    detrended = segments - trends

    if multifractal is True:
        var = np.var(detrended, axis=1)
        fluctuation = np.float_power(np.mean(np.float_power(var, q / 2), axis=1) / 2, 1 / q.T)
        fluctuation = np.mean(fluctuation)  # Average over qs (not sure of that!)

    else:
        # Compute Root Mean Square (RMS)
        fluctuation = np.sum(detrended ** 2, axis=1) / detrended.shape[1]
        fluctuation = np.sqrt(np.sum(fluctuation) / len(fluctuation))

    return fluctuation