Python numpy.mean() Examples

def _cascade_evaluation(self, X_test, y_test):
        """ Evaluate the accuracy of the cascade using X and y.

        :param X_test: np.array
            Array containing the test input samples.
            Must be of the same shape as training data.

        :param y_test: np.array
            Test target values.

        :return: float
            the cascade accuracy.
        """
        casc_pred_prob = np.mean(self.cascade_forest(X_test), axis=0)
        casc_pred = np.argmax(casc_pred_prob, axis=1)
        casc_accuracy = accuracy_score(y_true=y_test, y_pred=casc_pred)
        print('Layer validation accuracy = {}'.format(casc_accuracy))

        return casc_accuracy 

def mypsd(Rates,time_range,bin_w = 5., nmax = 4000):

      bins = np.arange(0,len(time_range),1)
      #print bins
      a,b = np.histogram(Rates, bins)
      ff = (1./len(bins))*abs(np.fft.fft(Rates- np.mean(Rates)))**2
      Fs = 1./(1*0.001)
      freq2 = np.fft.fftfreq(len(bins))[0:len(bins/2)+1] # d= dt
      freq = np.fft.fftfreq(len(bins))[:len(ff)/2+1]
      px = ff[0:len(ff)/2+1]
      max_px = np.max(px[1:])
      idx = px == max_px
      corr_freq = freq[pl.find(idx)]
      new_px = px
      max_pow = new_px[pl.find(idx)]
      return new_px,freq,corr_freq[0],freq2, max_pow 

def evaluate(self, dataset):
        predictions = self.predict(dataset[:,0])
        confusion_matrix = sklearn_confusion_matrix(dataset[:,1], predictions, labels=self.__classes)

        precisions = []
        recalls = []
        accuracies = []

        for gender in self.__classes:
            idx = self.__classes_indexes[gender]
            precision = 1
            recall = 1
            if np.sum(confusion_matrix[idx,:]) > 0:
                precision = confusion_matrix[idx][idx]/np.sum(confusion_matrix[idx,:])
            if np.sum(confusion_matrix[:, idx]) > 0:
                recall = confusion_matrix[idx][idx]/np.sum(confusion_matrix[:, idx])
            precisions.append(precision)
            recalls.append(recall)

        precision = np.mean(precisions)
        recall = np.mean(recalls)
        f1 = (2*(precision*recall))/float(precision+recall)
        accuracy = np.sum(confusion_matrix.diagonal())/float(np.sum(confusion_matrix))

        return precision, recall, accuracy, f1 

def compute_nystrom(ds_name, use_node_labels, embedding_dim, community_detection_method, kernels):
	if ds_name=="SYNTHETIC":
		graphs, labels = generate_synthetic()
	else:
		graphs, labels = load_data(ds_name, use_node_labels)
	communities, subgraphs = compute_communities(graphs, use_node_labels, community_detection_method)

	print("Number of communities: ", len(communities))
	lens = []
	for community in communities:
		lens.append(community.number_of_nodes())

	print("Average size: %.2f" % np.mean(lens))
	Q=[]
	for idx, k in enumerate(kernels):
		model = Nystrom(k, n_components=embedding_dim)
		model.fit(communities)
		Q_t = model.transform(communities)
		Q_t = np.vstack([np.zeros(embedding_dim), Q_t])
		Q.append(Q_t)

	return Q, subgraphs, labels, Q_t.shape 

def xloads(self):
        # Xloadings
        A = self.data_.transpose().values
        B = self.fscores.transpose().values
        A_mA = A - A.mean(1)[:, None]
        B_mB = B - B.mean(1)[:, None]

        ssA = (A_mA**2).sum(1)
        ssB = (B_mB**2).sum(1)

        xloads_ = (np.dot(A_mA, B_mB.T) /
                   np.sqrt(np.dot(ssA[:, None], ssB[None])))
        xloads = pd.DataFrame(
            xloads_, index=self.manifests, columns=self.latent)

        return xloads 

def encode_and_store(batch_x, output_dir, file_name):
	"""
	Args:
		1. batch_x:			Batch of 32*32 images which will go inside our autoencoder.
		2. output_dir:		Dir path for storing all encoded features for given `batch_x`.
							Features will be stored in the form of JSON file.
		3. file_name:		File name of JSON file.
	"""
	global AUTO_ENCODER
	if AUTO_ENCODER is None:
		load_AE()

	norm_batch = np.zeros(batch_x.shape)
	for i in range(len(batch_x)):
		norm_batch[i] = (batch_x[i] - np.mean(batch_x[i])) / np.std(batch_x[i])

	output_dict = {
		'name' : file_name,
		'encoded': AUTO_ENCODER.transform(norm_batch).tolist()}

	with open(output_dir+file_name+'.json', 'w') as f:
		json.dump(output_dict, f) 

def plot_qual(ax, quallist, invert=False):
    '''
    Create a FastQC-like "?Per base sequence quality?" plot
    Plot average quality per position
    zip will stop when shortest read is exhausted
    '''
    sns.set_style("darkgrid")
    if invert:
        l_Q, = ax.plot(np.array([np.mean(position) for position in zip(
            *[list(reversed(read)) for read in quallist])]), 'orange', label="Quality")
        ax.set_xlabel('Position in read from end')
        ax.set_xticklabels(-1 * ax.get_xticks().astype(int))
    else:
        l_Q, = ax.plot(np.array([np.mean(position)
                                 for position in zip(*quallist)]), 'orange', label="Quality")
        ax.set_xlabel('Position in read from start')
    return l_Q 

def update_data_sort_order(self, new_sort_order=None):
        if new_sort_order is not None:
            self.current_order = new_sort_order
        self.update_sort_idcs()
        self.data_image.set_extent((self.raw_lags[0], self.raw_lags[-1],
                            0, len(self.sort_idcs)))
        self.data_ax.set_ylim(0, len(self.sort_idcs))
        all_raw_data  = self.raw_data
        all_raw_data /= (1 + self.raw_data.mean(1)[:, np.newaxis])
        if len(all_raw_data) > 0:
            cmax          = 0.5*all_raw_data.max()
            cmin          = 0.5*all_raw_data.min()
            all_raw_data  = all_raw_data[self.sort_idcs, :]
        else:
            cmin = 0
            cmax = 1
        self.data_image.set_data(all_raw_data)
        self.data_image.set_clim(cmin, cmax)
        self.data_selection.set_y(len(self.sort_idcs)-len(self.selected_points))
        self.data_selection.set_height(len(self.selected_points))
        self.update_data_plot() 

def get_color_medio(self, roi, a,b,imprimir = False):
        xl,yl,ch = roi.shape
        roiyuv = cv2.cvtColor(roi,cv2.COLOR_RGB2YUV)
        roihsv = cv2.cvtColor(roi,cv2.COLOR_RGB2HSV)
        h,s,v=cv2.split(roihsv)
        mask=(h<5)
        h[mask]=200
        
        roihsv = cv2.merge((h,s,v))
        std = np.std(roiyuv.reshape(xl*yl,3),axis=0)
        media = np.mean(roihsv.reshape(xl*yl,3), axis=0)-60
        mediayuv = np.mean(roiyuv.reshape(xl*yl,3), axis=0)

        if std[0]<12 and std[1]<12 and std[2]<12:
        #if (std[0]<15 and std[2]<15) or ((media[0]>100 or media[0]<25) and (std[0]>10)):
            media = np.mean(roihsv.reshape(xl*yl,3), axis=0)
            # el amarillo tiene 65 de saturacion y sobre 200
            if media[1]<60: #and (abs(media[0]-30)>10):
                # blanco
                return [-10,0,0]
            else:
                return media
        else:
            return None 

def information_ratio(algorithm_returns, benchmark_returns):
    """
    http://en.wikipedia.org/wiki/Information_ratio

    Args:
        algorithm_returns (np.array-like):
            All returns during algorithm lifetime.
        benchmark_returns (np.array-like):
            All benchmark returns during algo lifetime.

    Returns:
        float. Information ratio.
    """
    relative_returns = algorithm_returns - benchmark_returns

    relative_deviation = relative_returns.std(ddof=1)

    if zp_math.tolerant_equals(relative_deviation, 0) or \
       np.isnan(relative_deviation):
        return 0.0

    return np.mean(relative_returns) / relative_deviation 

def testStartStopModulation(self):
        radiusInMilliRad= 12.4
        frequencyInHz= 100.
        centerInMilliRad= [-10, 15]
        self._tt.setTargetPosition(centerInMilliRad)
        self._tt.startModulation(radiusInMilliRad,
                                 frequencyInHz,
                                 centerInMilliRad)
        self.assertTrue(
            np.allclose(
                [1, 1, 0],
                self._ctrl.getWaveGeneratorStartStopMode()))
        waveform= self._ctrl.getWaveform(1)
        wants= self._tt._milliRadToGcsUnitsOneAxis(-10, self._tt.AXIS_A)
        got= np.mean(waveform)
        self.assertAlmostEqual(
            wants, got, msg="wants %g, got %g" % (wants, got))
        wants= self._tt._milliRadToGcsUnitsOneAxis(-10 + 12.4, self._tt.AXIS_A)
        got= np.max(waveform)
        self.assertAlmostEqual(
            wants, got, msg="wants %g, got %g" % (wants, got))

        self._tt.stopModulation()
        self.assertTrue(
            np.allclose(centerInMilliRad, self._tt.getTargetPosition())) 

def monitor(data_feeder):
    """
    Cost and time of test_fn on a given dataset section.
    Pass only one of `valid_feeder` or `test_feeder`.
    Don't pass `train_feed`.

    :returns:
        Mean cost over the input dataset (data_feeder)
        Total time spent
    """
    _total_time = time()
    _h0 = numpy.zeros((BATCH_SIZE, N_RNN, H0_MULT*DIM), dtype='float32')
    _big_h0 = numpy.zeros((BATCH_SIZE, N_RNN, H0_MULT*BIG_DIM), dtype='float32')
    _costs = []
    _data_feeder = load_data(data_feeder)
    for _seqs, _reset, _mask in _data_feeder:
        _cost, _big_h0, _h0 = test_fn(_seqs, _big_h0, _h0, _reset, _mask)
        _costs.append(_cost)

    return numpy.mean(_costs), time() - _total_time 

def monitor(data_feeder):
    """
    Cost and time of test_fn on a given dataset section.
    Pass only one of `valid_feeder` or `test_feeder`.
    Don't pass `train_feed`.

    :returns:
        Mean cost over the input dataset (data_feeder)
        Total time spent
    """
    _total_time = time()
    _h0 = numpy.zeros((BATCH_SIZE, N_RNN, H0_MULT*DIM), dtype='float32')
    _costs = []
    _data_feeder = load_data(data_feeder)
    for _seqs, _reset, _mask in _data_feeder:
        _cost, _h0 = test_fn(_seqs, _h0, _reset, _mask)
        _costs.append(_cost)

    return numpy.mean(_costs), time() - _total_time 

def monitor(data_feeder):
    """
    Cost and time of test_fn on a given dataset section.
    Pass only one of `valid_feeder` or `test_feeder`.
    Don't pass `train_feed`.

    :returns:
        Mean cost over the input dataset (data_feeder)
        Total time spent
    """
    _total_time = time()
    _h0 = numpy.zeros((BATCH_SIZE, N_RNN, H0_MULT*DIM), dtype='float32')
    _costs = []
    _data_feeder = load_data(data_feeder)
    for _seqs, _reset, _mask in _data_feeder:
        _cost, _h0 = test_fn(_seqs, _h0, _reset, _mask)
        _costs.append(_cost)

    return numpy.mean(_costs), time() - _total_time 

def doesnt_match(self, words):
        """
        Which word from the given list doesn't go with the others?

        Example::
          >>> trained_model.doesnt_match("breakfast cereal dinner lunch".split())
          'cereal'
        """
        words = [word for word in words if word in self.vocab]  # filter out OOV words
        logger.debug("using words %s" % words)
        if not words:
            raise ValueError("cannot select a word from an empty list")
        # which word vector representation is furthest away from the mean?
        selection = self.syn0norm[[self.vocab[word].index for word in words]]
        mean = np.mean(selection, axis=0)
        sim = np.dot(selection, mean / np.linalg.norm(mean))
        return words[np.argmin(sim)] 

def effective_sample_size(x, mu, var, logger):
    """
    Calculate the effective sample size of sequence generated by MCMC.
    :param x:
    :param mu: mean of the variable
    :param var: variance of the variable
    :param logger: logg
    :return: effective sample size of the sequence
    Make sure that `mu` and `var` are correct!
    """
    # batch size, time, dimension
    b, t, d = x.shape
    ess_ = np.ones([d])
    for s in range(1, t):
        p = auto_correlation_time(x, s, mu, var)
        if np.sum(p > 0.05) == 0:
            break
        else:
            for j in range(0, d):
                if p[j] > 0.05:
                    ess_[j] += 2.0 * p[j] * (1.0 - float(s) / t)

    logger.info('ESS: max [%f] min [%f] / [%d]' % (t / np.min(ess_), t / np.max(ess_), t))
    return t / ess_ 

def apply(self, referenceSamples=None, testSamples=None, gaussianCenters=None) :
        """
        Calculates the alpha-relative Pearson divergence score
        """
        densityRatioEstimator         = AlphaRelativeDensityRatioEstimator(self.alphaConstraint  ,
                                                                           self.sigmaWidth       ,
                                                                           self.lambdaRegularizer,
                                                                           self.kernelBasis      )

        # Estimate alpha relative density ratio and pearson divergence score
        (r_alpha_Xref, r_alpha_Xtest) = densityRatioEstimator.apply(referenceSamples, testSamples, gaussianCenters)

        PE_divergence = ( numpy.mean(r_alpha_Xref) -
                          ( 0.5 * ( self.alphaConstraint * numpy.mean(r_alpha_Xref ** 2) +
                            (1.0 - self.alphaConstraint) * numpy.mean(r_alpha_Xtest ** 2) ) ) - 0.5)

        return (PE_divergence, r_alpha_Xtest) 

def test(self, input_path, output_path):
        if not self.load()[0]:
            raise Exception("No model is found, please train first")
            
        mean, std = self.sess.run([self.mean, self.std])
        
        images = np.empty((1, self.im_size[0], self.im_size[1], self.im_size[2], 1), dtype=np.float32)
        #labels = np.empty((1, self.im_size[0], self.im_size[1], self.im_size[2], self.nclass), dtype=np.float32)
        for f in input_path:
            images[0, ..., 0], read_info = read_testing_inputs(f, self.roi[0], self.im_size, output_path)
            probs = self.sess.run(self.probs, feed_dict = { self.images: (images - mean) / std,
                                                            self.is_training: True,
                                                            self.keep_prob: 1 })
            #print(self.roi[1] + os.path.basename(f) + ":" + str(dice))
            output_file = os.path.join(output_path, self.roi[1] + '_' + os.path.basename(f))
            f_h5 = h5py.File(output_file, 'w')
            if self.roi[0] < 0:
                f_h5['predictions'] = restore_labels(np.argmax(probs[0], 3), self.roi[0], read_info)
            else:
                f_h5['probs'] = restore_labels(probs[0, ..., 1], self.roi[0], read_info)
            f_h5.close() 

def transfer_color(content, style):
    import scipy.linalg as sl
    # Mean and covariance of content
    content_mean = np.mean(content, axis = (0, 1))
    content_diff = content - content_mean
    content_diff = np.reshape(content_diff, (-1, content_diff.shape[2]))
    content_covariance = np.matmul(content_diff.T, content_diff) / (content_diff.shape[0])

    # Mean and covariance of style
    style_mean = np.mean(style, axis = (0, 1))
    style_diff = style - style_mean
    style_diff = np.reshape(style_diff, (-1, style_diff.shape[2]))
    style_covariance = np.matmul(style_diff.T, style_diff) / (style_diff.shape[0])

    # Calculate A and b
    A = np.matmul(sl.sqrtm(content_covariance), sl.inv(sl.sqrtm(style_covariance)))
    b = content_mean - np.matmul(A, style_mean)

    # Construct new style
    new_style = np.reshape(style, (-1, style.shape[2])).T
    new_style = np.matmul(A, new_style).T
    new_style = np.reshape(new_style, style.shape)
    new_style = new_style + b

    return new_style 

def get_selective_mirrors(self, number=None):
        """get mirror genotypic directions from worst solutions.

        Details:

        To be called after the mean has been updated.

        Takes the last ``number=sp.lam_mirr`` entries in the
        ``self.pop[self.fit.idx]`` as solutions to be mirrored.

        Do not take a mirror if it is suspected to stem from a
        previous mirror in order to not go endlessly back and forth.
        """
        if number is None:
            number = self.sp.lam_mirr
        if not hasattr(self, '_indices_of_selective_mirrors'):
            self._indices_of_selective_mirrors = []
        res = []
        for i in range(1, number + 1):
            if 'all-selective-mirrors' in self.opts['vv'] or self.fit.idx[-i] not in self._indices_of_selective_mirrors:
                res.append(self.mean_old - self.pop[self.fit.idx[-i]])
        assert len(res) >= number - len(self._indices_of_selective_mirrors)
        return res

    # ____________________________________________________________ 

def result(self):
        """return a `CMAEvolutionStrategyResult` `namedtuple`.

        :See: `cma.evolution_strategy.CMAEvolutionStrategyResult`
            or try ``help(...result)`` on the ``result`` property
            of an `CMAEvolutionStrategy` instance or on the
            `CMAEvolutionStrategyResult` instance itself.

        """
        # TODO: how about xcurrent?
        # return CMAEvolutionStrategyResult._generate(self)
        res = self.best.get() + (  # (x, f, evals) triple
            self.countevals,
            self.countiter,
            self.gp.pheno(self.mean),
            self.gp.scales * self.sigma * self.sigma_vec.scaling *
                self.dC**0.5)
        try:
            return CMAEvolutionStrategyResult(*res)
        except NameError:
            return res 

def result_pretty(self, number_of_runs=0, time_str=None,
                      fbestever=None):
        """pretty print result.

        Returns `result` of ``self``.

        """
        if fbestever is None:
            fbestever = self.best.f
        s = (' after %i restart' + ('s' if number_of_runs > 1 else '')) \
            % number_of_runs if number_of_runs else ''
        for k, v in self.stop().items():
            print('termination on %s=%s%s' % (k, str(v), s +
                  (' (%s)' % time_str if time_str else '')))

        print('final/bestever f-value = %e %e' % (self.best.last.f,
                                                  fbestever))
        if self.N < 9:
            print('incumbent solution: ' + str(list(self.gp.pheno(self.mean, into_bounds=self.boundary_handler.repair))))
            print('std deviation: ' + str(list(self.sigma * self.sigma_vec.scaling * np.sqrt(self.dC) * self.gp.scales)))
        else:
            print('incumbent solution: %s ...]' % (str(self.gp.pheno(self.mean, into_bounds=self.boundary_handler.repair)[:8])[:-1]))
            print('std deviations: %s ...]' % (str((self.sigma * self.sigma_vec.scaling * np.sqrt(self.dC) * self.gp.scales)[:8])[:-1]))
        return self.result 

def isotropic_mean_shift(self):
        """normalized last mean shift, under random selection N(0,I)

        distributed.

        Caveat: while it is finite and close to sqrt(n) under random
        selection, the length of the normalized mean shift under
        *systematic* selection (e.g. on a linear function) tends to
        infinity for mueff -> infty. Hence it must be used with great
        care for large mueff.
        """
        z = self.sm.transform_inverse((self.mean - self.mean_old) /
                                      self.sigma_vec.scaling)
        # works unless a re-parametrisation has been done
        # assert Mh.vequals_approximately(z, np.dot(es.B, (1. / es.D) *
        #         np.dot(es.B.T, (es.mean - es.mean_old) / es.sigma_vec)))
        z /= self.sigma * self.sp.cmean
        z *= self.sp.weights.mueff**0.5
        return z 

def sample(self, number, lazy_update_gap=None, same_length=False):
        self.update_now(lazy_update_gap)
        arz = self.randn(number, self.dimension)
        if same_length:
            if same_length is True:
                len_ = self.chiN
            else:
                len_ = same_length # presumably N**0.5, useful if self.opts['CSA_squared']
            for i in rglen(arz):
                ss = sum(arz[i]**2)
                if 1 < 3 or ss > self.N + 10.1:
                    arz[i] *= len_ / ss**0.5
            # or to average
            # arz *= 1 * self.const.chiN / np.mean([sum(z**2)**0.5 for z in arz])
        ary = np.dot(self.B, (self.D * arz).T).T
        # self.ary = ary  # needed whatfor?
        return ary 

def norm(self, x):
        """compute the Mahalanobis norm that is induced by the
        statistical model / sample distribution, specifically by
        covariance matrix ``C``. The expected Mahalanobis norm is
        about ``sqrt(dimension)``.

        Example
        -------
        >>> import cma, numpy as np
        >>> sm = cma.sampler.GaussFullSampler(np.ones(10))
        >>> x = np.random.randn(10)
        >>> d = sm.norm(x)

        `d` is the norm "in" the true sample distribution,
        sampled points have a typical distance of ``sqrt(2*sm.dim)``,
        where ``sm.dim`` is the dimension, and an expected distance of
        close to ``dim**0.5`` to the sample mean zero. In the example,
        `d` is the Euclidean distance, because C = I.
        """
        return sum((np.dot(self.B.T, x) / self.D)**2)**0.5 

def sample(self, number, same_length=False):
        arz = self.randn(number, self.dimension)
        if same_length:
            if same_length is True:
                len_ = self.chin
            else:
                len_ = same_length # presumably N**0.5, useful if self.opts['CSA_squared']
            for i in rglen(arz):
                ss = sum(arz[i]**2)
                if 1 < 3 or ss > self.N + 10.1:
                    arz[i] *= len_ / ss**0.5
            # or to average
            # arz *= 1 * self.const.chiN / np.mean([sum(z**2)**0.5 for z in arz])
        ary = self.C**0.5 * arz
        # self.ary = ary  # needed whatfor?
        return ary 

def norm(self, x):
        """compute the Mahalanobis norm that is induced by the
        statistical model / sample distribution, specifically by
        covariance matrix ``C``. The expected Mahalanobis norm is
        about ``sqrt(dimension)``.

        Example
        -------
        >>> import cma, numpy as np
        >>> sm = cma.sampler.GaussFullSampler(np.ones(10))
        >>> x = np.random.randn(10)
        >>> d = sm.norm(x)

        `d` is the norm "in" the true sample distribution,
        sampled points have a typical distance of ``sqrt(2*sm.dim)``,
        where ``sm.dim`` is the dimension, and an expected distance of
        close to ``dim**0.5`` to the sample mean zero. In the example,
        `d` is the Euclidean distance, because C = I.
        """
        return sum(np.asarray(x)**2 / self.C)**0.5 

def update_measure(self):
        """updated noise level measure using two fitness lists ``self.fit`` and
        ``self.fitre``, return ``self.noiseS, all_individual_measures``.

        Assumes that ``self.idx`` contains the indices where the fitness
        lists differ.

        """
        lam = len(self.fit)
        idx = np.argsort(self.fit + self.fitre)
        ranks = np.argsort(idx).reshape((2, lam))
        rankDelta = ranks[0] - ranks[1] - np.sign(ranks[0] - ranks[1])

        # compute rank change limits using both ranks[0] and ranks[1]
        r = np.arange(1, 2 * lam)  # 2 * lam - 2 elements
        limits = [0.5 * (Mh.prctile(np.abs(r - (ranks[0, i] + 1 - (ranks[0, i] > ranks[1, i]))),
                                      self.theta * 50) +
                         Mh.prctile(np.abs(r - (ranks[1, i] + 1 - (ranks[1, i] > ranks[0, i]))),
                                      self.theta * 50))
                    for i in self.idx]
        # compute measurement
        #                               max: 1 rankchange in 2*lambda is always fine
        s = np.abs(rankDelta[self.idx]) - Mh.amax(limits, 1)  # lives roughly in 0..2*lambda
        self.noiseS += self.cum * (np.mean(s) - self.noiseS)
        return self.noiseS, s 

def mse(ypredict, ytrue):
    """
    >>> mse(1.0, 3.0)
    4.0
    """
    diff = ypredict - ytrue
    return np.mean(diff**2) 

def compute_score(self, gts, res):
        """
        Computes Rouge-L score given a set of reference and candidate sentences for the dataset
        Invoked by evaluate_captions.py
        
        :param hypo_for_image: dict : candidate / test sentences with "image name" key and "tokenized sentences" as values
        :param ref_for_image: dict : reference MS-COCO sentences with "image name" key and "tokenized sentences" as values
        :returns: average_score: float (mean ROUGE-L score computed by averaging scores for all the images)
        """
        assert(gts.keys() == res.keys())
        imgIds = gts.keys()

        score = []
        for id in imgIds:
            hypo = res[id]
            ref  = gts[id]

            score.append(self.calc_score(hypo, ref))

            # Sanity check.
            assert(type(hypo) is list)
            assert(len(hypo) == 1)
            assert(type(ref) is list)
            assert(len(ref) > 0)

        average_score = np.mean(np.array(score))
        return average_score, np.array(score) 

def analytic_convolution_gaussian(mu1,covar1,mu2,covar2):
    """
    The analytic vconvolution of two Gaussians is simply the sum of the two mean vectors
    and the two convariance matrixes

    --- INPUT ---
    mu1         The mean of the first gaussian
    covar1      The covariance matrix of of the first gaussian
    mu2         The mean of the second gaussian
    covar2      The covariance matrix of of the second gaussian

    """
    muconv    = mu1+mu2
    covarconv = covar1+covar2
    return muconv, covarconv

# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 

def reshape_array(array, newsize, pixcombine='sum'):
    """
    Reshape an array to a give size using either the sum, mean or median of the pixels binned

    Note that the old array dimensions have to be multiples of the new array dimensions

    --- INPUT ---
    array           Array to reshape (combine pixels)
    newsize         New size of array
    pixcombine      The method to combine the pixels with. Choices are sum, mean and median

    """
    sh = newsize[0],array.shape[0]//newsize[0],newsize[1],array.shape[1]//newsize[1]
    pdb.set_trace()
    if pixcombine == 'sum':
        reshapedarray = array.reshape(sh).sum(-1).sum(1)
    elif pixcombine == 'mean':
        reshapedarray = array.reshape(sh).mean(-1).mean(1)
    elif pixcombine == 'median':
        reshapedarray = array.reshape(sh).median(-1).median(1)

    return reshapedarray
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 

def smooth_colors(src, dst, src_l):
    blur_amount = BLUR_FRACTION * np.linalg.norm(np.mean(src_l[LEFT_EYE_IDX], axis = 0) - np.mean(src_l[RIGHT_EYE_IDX], axis = 0))
    blur_amount = (int)(blur_amount)

    if blur_amount % 2 == 0:
        blur_amount += 1

    src_blur = cv2.GaussianBlur(src, (blur_amount, blur_amount), 0)
    dst_blur = cv2.GaussianBlur(dst, (blur_amount, blur_amount), 0)

    dst_blur += (128 * ( dst_blur <= 1.0 )).astype(dst_blur.dtype)

    return (np.float64(dst) * np.float64(src_blur)/np.float64(dst_blur)) 

def get_tm_opp(pts1, pts2):
    # Transformation matrix - ( Translation + Scaling + Rotation )
    # using Procuster analysis
    pts1 = np.float64(pts1)
    pts2 = np.float64(pts2)

    m1 = np.mean(pts1, axis = 0)
    m2 = np.mean(pts2, axis = 0)

    # Removing translation
    pts1 -= m1
    pts2 -= m2

    std1 = np.std(pts1)
    std2 = np.std(pts2)
    std_r = std2/std1

    # Removing scaling
    pts1 /= std1
    pts2 /= std2

    U, S, V = np.linalg.svd(np.transpose(pts1) * pts2)

    # Finding the rotation matrix
    R = np.transpose(U * V)

    return np.vstack([np.hstack((std_r * R,
        np.transpose(m2) - std_r * R * np.transpose(m1))), np.matrix([0.0, 0.0, 1.0])]) 

def fit(self, x):
        s = x.shape
        x = x.copy().reshape((s[0],np.prod(s[1:])))
        m = np.mean(x, axis=0)
        x -= m
        sigma = np.dot(x.T,x) / x.shape[0]
        U, S, V = linalg.svd(sigma)
        tmp = np.dot(U, np.diag(1./np.sqrt(S+self.regularization)))
        tmp2 = np.dot(U, np.diag(np.sqrt(S+self.regularization)))
        self.ZCA_mat = th.shared(np.dot(tmp, U.T).astype(th.config.floatX))
        self.inv_ZCA_mat = th.shared(np.dot(tmp2, U.T).astype(th.config.floatX))
        self.mean = th.shared(m.astype(th.config.floatX)) 

def apply(self, x):
        s = x.shape
        if isinstance(x, np.ndarray):
            return np.dot(x.reshape((s[0],np.prod(s[1:]))) - self.mean.get_value(), self.ZCA_mat.get_value()).reshape(s)
        elif isinstance(x, T.TensorVariable):
            return T.dot(x.flatten(2) - self.mean.dimshuffle('x',0), self.ZCA_mat).reshape(s)
        else:
            raise NotImplementedError("Whitening only implemented for numpy arrays or Theano TensorVariables") 

def invert(self, x):
        s = x.shape
        if isinstance(x, np.ndarray):
            return (np.dot(x.reshape((s[0],np.prod(s[1:]))), self.inv_ZCA_mat.get_value()) + self.mean.get_value()).reshape(s)
        elif isinstance(x, T.TensorVariable):
            return (T.dot(x.flatten(2), self.inv_ZCA_mat) + self.mean.dimshuffle('x',0)).reshape(s)
        else:
            raise NotImplementedError("Whitening only implemented for numpy arrays or Theano TensorVariables")

# T.nnet.relu has some issues with very large inputs, this is more stable 

def softmax_loss(p_true, output_before_softmax):
    output_before_softmax -= T.max(output_before_softmax, axis=1, keepdims=True)
    if p_true.ndim==2:
        return T.mean(T.log(T.sum(T.exp(output_before_softmax),axis=1)) - T.sum(p_true*output_before_softmax, axis=1))
    else:
        return T.mean(T.log(T.sum(T.exp(output_before_softmax),axis=1)) - output_before_softmax[T.arange(p_true.shape[0]),p_true]) 

def get_output_for(self, input, **kwargs):
        return T.mean(input, axis=(2,3)) 

def run_epoch_doc(docs, labels, tags, tm, pad_id, cf):
    batches = int(math.ceil(float(len(docs))/cf.batch_size))
    accs = []
    for b in xrange(batches):
        d, y, m, t, num_docs = get_batch_doc(docs, labels, tags, b, cf.doc_len, cf.tag_len, cf.batch_size, pad_id)
        prob = sess.run(tm.sup_probs, {tm.doc:d, tm.label:y, tm.sup_mask: m, tm.tag: t})
        pred = np.argmax(prob, axis=1)
        accs.extend(pred[:num_docs] == y[:num_docs])

    print "\ntest classification accuracy = %.3f" % np.mean(accs) 

def print_corpus_stats(name, sents, docs, stats):
    print name + ":"
    print "\tno. of docs =", len(docs[0])
    if len(sents[0]) > 0:
        print "\ttopic model no. of sequences =", len(sents[0])
        print "\ttopic model no. of tokens =", sum([ len(item[2])-1 for item in sents[0] ])
        print "\toriginal doc mean len =", stats[3]
        print "\toriginal doc max len =", stats[4]
        print "\toriginal doc min len =", stats[5]
    if len(sents[1]) > 0:
        print "\tlanguage model no. of sequences =", len(sents[1])
        print "\tlanguage model no. of tokens =", sum([ len(item[2])-1 for item in sents[1] ])
        print "\toriginal sent mean len =", stats[0]
        print "\toriginal sent max len =", stats[1]
        print "\toriginal sent min len =", stats[2] 

def MSE(self, responses):
        mean = np.mean(responses, axis=0)
        return np.mean((responses - mean) ** 2) 

def make_leaf(self, responses):
        self.leaf = np.mean(responses, axis=0) 

def predict(self, point):
        response = []
        for i in range(self.ntrees):
            response.append(self.trees[i].predict(point))
        return np.mean(response, axis=0) 

def normaliza(self, X):
        correction = np.sqrt((len(X) - 1) / len(X))  # std factor corretion
        mean_ = np.mean(X, 0)
        scale_ = np.std(X, 0)
        X = X - mean_
        X = X / (scale_ * correction)
        return X 

def gof(self):
        r2mean = np.mean(self.r2.T[self.endoexo()[0]].values)
        AVEmean = self.AVE().copy()

        totalblock = 0
        for i in range(self.lenlatent):
            block = self.data_[self.Variables['measurement']
                               [self.Variables['latent'] == self.latent[i]]]
            block = len(block.columns.values)
            totalblock += block
            AVEmean[self.latent[i]] = AVEmean[self.latent[i]] * block

        AVEmean = np.sum(AVEmean) / totalblock
        return np.sqrt(AVEmean * r2mean) 

def srmr(self):
        srmr = (self.empirical() - self.implied())
        srmr = np.sqrt(((srmr.values) ** 2).mean())
        return srmr 

def dataInfo(self):
        sd_ = np.std(self.data, 0)
        mean_ = np.mean(self.data, 0)
        skew = scipy.stats.skew(self.data)
        kurtosis = scipy.stats.kurtosis(self.data)
        w = [scipy.stats.shapiro(self.data.ix[:, i])[0]
             for i in range(len(self.data.columns))]

        return [mean_, sd_, skew, kurtosis, w] 

def htmt(self):

        htmt_ = pd.DataFrame(pd.DataFrame.corr(self.data_),
                             index=self.manifests, columns=self.manifests)

        mean = []
        allBlocks = []
        for i in range(self.lenlatent):
            block_ = self.Variables['measurement'][
                self.Variables['latent'] == self.latent[i]]
            allBlocks.append(list(block_.values))
            block = htmt_.ix[block_, block_]
            mean_ = (block - np.diag(np.diag(block))).values
            mean_[mean_ == 0] = np.nan
            mean.append(np.nanmean(mean_))

        comb = [[k, j] for k in range(self.lenlatent)
                for j in range(self.lenlatent)]

        comb_ = [(np.sqrt(mean[comb[i][1]] * mean[comb[i][0]]))
                 for i in range(self.lenlatent ** 2)]

        comb__ = []
        for i in range(self.lenlatent ** 2):
            block = (htmt_.ix[allBlocks[comb[i][1]],
                              allBlocks[comb[i][0]]]).values
#            block[block == 1] = np.nan
            comb__.append(np.nanmean(block))

        htmt__ = np.divide(comb__, comb_)
        where_are_NaNs = np.isnan(htmt__)
        htmt__[where_are_NaNs] = 0

        htmt = pd.DataFrame(np.tril(htmt__.reshape(
            (self.lenlatent, self.lenlatent)), k=-1), index=self.latent, columns=self.latent)

        return htmt 

def obs_callback(self, msg):
        cn0 = np.array([obs.cn0/4.0 for obs in msg.obs])

        m = SignalStatus()
        m.header.stamp = rospy.Time.now()
        m.mean_cn0 = np.mean(cn0)
        m.median_cn0 = np.median(cn0)
        m.robust_mean_cn0 = np.mean(reject_outliers(cn0))
        m.num_sats = len(msg.obs)
        self.signal_pub.publish(m)