Python numpy.std方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def __init__(self, bam, keepReads=False):
        self.insertSizes = []
        self.readLengths = []
        self.orientations = []
        self._insertSizeKDE = None
        self.singleEnded = False

        self._insertSizeScores = {} # cache

        try:
            self.insertSizes, self.reads, self.orientations, self.readLengths = sampleInsertSizes(bam, keepReads=keepReads)
            if len(self.insertSizes) > 1:
                logging.info("  insert size mean: {:.2f} std: {:.2f}".format(numpy.mean(self.insertSizes), numpy.std(self.insertSizes)))
        except ValueError as e:
            print("*"*100, "here")
            print("ERROR:", e) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def validate_on_lfw(model, lfw_160_path):
    # Read the file containing the pairs used for testing
    pairs = lfw.read_pairs('validation-LFW-pairs.txt')
    # Get the paths for the corresponding images
    paths, actual_issame = lfw.get_paths(lfw_160_path, pairs)
    num_pairs = len(actual_issame)

    all_embeddings = np.zeros((num_pairs * 2, 512), dtype='float32')
    for k in tqdm.trange(num_pairs):
        img1 = cv2.imread(paths[k * 2], cv2.IMREAD_COLOR)[:, :, ::-1]
        img2 = cv2.imread(paths[k * 2 + 1], cv2.IMREAD_COLOR)[:, :, ::-1]
        batch = np.stack([img1, img2], axis=0)
        embeddings = model.eval_embeddings(batch)
        all_embeddings[k * 2: k * 2 + 2, :] = embeddings

    tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(
        all_embeddings, actual_issame, distance_metric=1, subtract_mean=True)

    print('Accuracy: %2.5f+-%2.5f' % (np.mean(accuracy), np.std(accuracy)))
    print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))

    auc = metrics.auc(fpr, tpr)
    print('Area Under Curve (AUC): %1.3f' % auc)
    eer = brentq(lambda x: 1. - x - interpolate.interp1d(fpr, tpr)(x), 0., 1.)
    print('Equal Error Rate (EER): %1.3f' % eer) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def get_graph_stats(graph_obj_handle, prop='degrees'):
    # if prop == 'degrees':
    num_cores = multiprocessing.cpu_count()
    inputs = [int(i*len(graph_obj_handle)/num_cores) for i in range(num_cores)] + [len(graph_obj_handle)]
    res = Parallel(n_jobs=num_cores)(delayed(get_values)(graph_obj_handle, inputs[i], inputs[i+1], prop) for i in range(num_cores))

    stat_dict = {}

    if 'degrees' in prop:
        stat_dict['degrees'] = list(set([d for core_res in res for file_res in core_res for d in file_res['degrees']]))
    if 'edge_labels' in prop:
        stat_dict['edge_labels'] = list(set([d for core_res in res for file_res in core_res for d in file_res['edge_labels']]))
    if 'target_mean' in prop or 'target_std' in prop:
        param = np.array([file_res['params'] for core_res in res for file_res in core_res])
    if 'target_mean' in prop:
        stat_dict['target_mean'] = np.mean(param, axis=0)
    if 'target_std' in prop:
        stat_dict['target_std'] = np.std(param, axis=0)

    return stat_dict 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def upper_bollinger_band(data, period, std_mult=2.0):
    """
    Upper Bollinger Band.

    Formula:
    u_bb = SMA(t) + STD(SMA(t-n:t)) * std_mult
    """
    check_for_period_error(data, period)

    period = int(period)
    simple_ma = sma(data, period)[period-1:]

    upper_bb = []
    for idx in range(len(data) - period + 1):
        std_dev = np.std(data[idx:idx + period])
        upper_bb.append(simple_ma[idx] + std_dev * std_mult)
    upper_bb = fill_for_noncomputable_vals(data, upper_bb)

    return np.array(upper_bb) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def lower_bollinger_band(data, period, std=2.0):
    """
    Lower Bollinger Band.

    Formula:
    u_bb = SMA(t) - STD(SMA(t-n:t)) * std_mult
    """
    check_for_period_error(data, period)

    period = int(period)
    simple_ma = sma(data, period)[period-1:]

    lower_bb = []
    for idx in range(len(data) - period + 1):
        std_dev = np.std(data[idx:idx + period])
        lower_bb.append(simple_ma[idx] - std_dev * std)
    lower_bb = fill_for_noncomputable_vals(data, lower_bb)

    return np.array(lower_bb) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def bandwidth(data, period, std=2.0):
    """
    Bandwidth.

    Formula:
    bw = u_bb - l_bb / m_bb
    """
    check_for_period_error(data, period)

    period = int(period)
    bandwidth = ((upper_bollinger_band(data, period, std) -
                 lower_bollinger_band(data, period, std)) /
                 middle_bollinger_band(data, period, std)
                 )

    return bandwidth 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def standard_deviation(data, period):
    """
    Standard Deviation.

    Formula:
    std = sqrt(avg(abs(x - avg(x))^2))
    """
    check_for_period_error(data, period)

    stds = list(map(
        lambda idx:
        np.std(data[idx+1-period:idx+1], ddof=1),
        range(period-1, len(data))
        ))

    stds = fill_for_noncomputable_vals(data, stds)
    return stds 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def log(self):
        end_idxs = np.nonzero(self._dones)[0] + 1

        returns = []

        start_idx = 0
        for end_idx in end_idxs:
            rewards = self._rewards[start_idx:end_idx]
            returns.append(np.sum(rewards))

            start_idx = end_idx

        logger.record_tabular('ReturnAvg', np.mean(returns))
        logger.record_tabular('ReturnStd', np.std(returns))
        logger.record_tabular('ReturnMin', np.min(returns))
        logger.record_tabular('ReturnMax', np.max(returns))

##################
### Tensorflow ###
################## 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def update_critic(self, ob_no, hidden, q_n):
        """
        given:
            self.num_value_iters
            self.l2_reg

        arguments:
            ob_no: (minibsize, history, meta_obs_dim)
            hidden: (minibsize, self.gru_size)
            q_n: (minibsize)

        requires:
            self.num_value_iters
        """
        target_n = (q_n - np.mean(q_n))/(np.std(q_n)+1e-8)
        for k in range(self.num_value_iters):
            critic_loss, _ = self.sess.run(
                [self.critic_loss, self.critic_update_op],
                feed_dict={self.sy_target_n: target_n, self.sy_ob_no: ob_no, self.sy_hidden: hidden})
        return critic_loss 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def get(self):
        tpr, fpr, accuracy, threshold = calculate_roc(
            self.thresholds, np.asarray(
                self.dists), np.asarray(
                self.issame), self.nfolds)

        val, val_std, far = calculate_val(
            self.thresholds, np.asarray(
                self.dists), np.asarray(
                self.issame), self.far_target, self.nfolds)

        acc, acc_std = np.mean(accuracy), np.std(accuracy)
        threshold = (
                1 - threshold) if self.dist_type == 'cosine' else threshold
        return tpr, fpr, acc, threshold, val, val_std, far, acc_std


# code below is modified from project <Facenet (David Sandberg)> and
# <Gluon-Face> 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def extractMeanDataStats(size = [200, 200, 100], 
						postfix = '_200x200x100orig', 
						main_folder_path = '../../Data/MS2017b/', 
						):
	scan_folders = glob.glob(main_folder_path + 'scans/*')
	img_path = 'pre/FLAIR' + postfix + '.nii.gz'
	segm_path = 'wmh' + postfix + '.nii.gz'
	
	shape_ = [len(scan_folders), size[0], size[1], size[2]]
	arr = np.zeros(shape_)

	for i, sf in enumerate(scan_folders):
		arr[i, :,:,:] =  numpyFromScan(os.path.join(sf,img_path)).squeeze()

	arr /= len(scan_folders)

	means = np.mean(arr)
	stds = np.std(arr, axis = 0)

	np.save(main_folder_path + 'extra_data/std' + postfix, stds)
	np.save(main_folder_path + 'extra_data/mean' + postfix, means) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def reverse_generator(generator, X_sample, y_sample, title):
    """Gradient descent to map images back to their latent vectors."""

    latent_vec = np.random.normal(size=(1, 100))

    # Function for figuring out how to bump the input.
    target = K.placeholder()
    loss = K.sum(K.square(generator.outputs[0] - target))
    grad = K.gradients(loss, generator.inputs[0])[0]
    update_fn = K.function(generator.inputs + [target], [grad])

    # Repeatedly apply the update rule.
    xs = []
    for i in range(60):
        print('%d: latent_vec mean=%f, std=%f'
              % (i, np.mean(latent_vec), np.std(latent_vec)))
        xs.append(generator.predict_on_batch([latent_vec, y_sample]))
        for _ in range(10):
            update_vec = update_fn([latent_vec, y_sample, X_sample])[0]
            latent_vec -= update_vec * update_rate

    # Plots the samples.
    xs = np.concatenate(xs, axis=0)
    plot_as_gif(xs, X_sample, title) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def addVariantResults(self, dataHub):
        variant = str(dataHub.variant)
        for sampleName, sample in dataHub.samples.items():
            counts = collections.Counter()
            reasons = {}
            alnScores = collections.defaultdict(list)
            insertSizes = collections.defaultdict(list)

            # collect stats
            for alnCollection in sample.alnCollections:
                allele = alnCollection.choice
                counts[allele] += 1

                if not allele in reasons:
                    reasons[allele] = collections.Counter()

                reasons[allele][alnCollection.why] += 1
                alnScores[allele].append(sum(aln.score for aln in alnCollection.chosenSet().getAlignments()))
                insertSizes[allele].append(len(alnCollection.chosenSet()))



            # record stats
            for allele, count in counts.items():
                self.stats.append([variant, sampleName, allele, "count", count])

            for allele in reasons:
                for reason in reasons[allele]:
                    self.stats.append([variant, sampleName, allele, "reason_{}".format(reason), reasons[allele][reason]])

            for allele in alnScores:
                self.stats.append([variant, sampleName, allele, "alnScore_mean", numpy.mean(alnScores[allele])])
                self.stats.append([variant, sampleName, allele, "alnScore_std", numpy.std(alnScores[allele])])

            for allele in insertSizes:
                self.stats.append([variant, sampleName, allele, "insertSize_mean", numpy.mean(insertSizes[allele])])
                self.stats.append([variant, sampleName, allele, "insertSize_std", numpy.std(insertSizes[allele])]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def bias_variance_decomposition(self, graphs, targets,
                                    cv=5, n_bootstraps=10):
        """bias_variance_decomposition."""
        x = self.transform(graphs)
        score_list = []
        for i in range(n_bootstraps):
            scores = cross_val_score(
                self.model, x, targets, cv=cv)
            score_list.append(scores)
        score_list = np.array(score_list)
        mean_scores = np.mean(score_list, axis=1)
        std_scores = np.std(score_list, axis=1)
        return mean_scores, std_scores 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import std [as 别名]
def output_avg_and_std(iterable):
    """output_avg_and_std."""
    print(('score: %.2f +-%.2f' % (np.mean(iterable), np.std(iterable))))
    return iterable