Python random.normalvariate方法代码示例

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def _get_gauss_data(n=10000, covariance=0.4, expand=True):
    """Generate correlated and uncorrelated Gaussian variables.

    Generate two sets of random normal data, where one set has a given
    covariance and the second is uncorrelated.
    """
    corr_expected = covariance / (1 * np.sqrt(covariance**2 + (1-covariance)**2))
    expected_mi = calculate_mi(corr_expected)
    src_corr = [rn.normalvariate(0, 1) for r in range(n)]  # correlated src
    src_uncorr = [rn.normalvariate(0, 1) for r in range(n)]  # uncorrelated src
    target = [sum(pair) for pair in zip(
                    [covariance * y for y in src_corr[0:n]],
                    [(1-covariance) * y for y in [
                        rn.normalvariate(0, 1) for r in range(n)]])]
    # Make everything numpy arrays so jpype understands it. Add an additional
    # axis if requested (MI/CMI estimators accept 2D arrays, TE/AIS only 1D).
    if expand:
        src_corr = np.expand_dims(np.array(src_corr), axis=1)
        src_uncorr = np.expand_dims(np.array(src_uncorr), axis=1)
        target = np.expand_dims(np.array(target), axis=1)
    else:
        src_corr = np.array(src_corr)
        src_uncorr = np.array(src_uncorr)
        target = np.array(target)
    return expected_mi, src_corr, src_uncorr, target 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def heston_construct_correlated_path(params: ModelParameters,
                                     brownian_motion_one: np.array):
    """
    This method is a simplified version of the Cholesky decomposition method
    for just two assets. It does not make use of matrix algebra and is therefore
    quite easy to implement.

    Arguments:
        params : ModelParameters
            The parameters for the stochastic model.
        brownian_motion_one : np.array
            (Not filled)

    Returns:
        A correlated brownian motion path.
    """
    # We do not multiply by sigma here, we do that in the Heston model
    sqrt_delta = np.sqrt(params.all_delta)
    # Construct a path correlated to the first path
    brownian_motion_two = []
    for i in range(params.all_time - 1):
        term_one = params.cir_rho * brownian_motion_one[i]
        term_two = np.sqrt(1 - pow(params.cir_rho, 2)) * random.normalvariate(0, sqrt_delta)
        brownian_motion_two.append(term_one + term_two)
    return np.array(brownian_motion_one), np.array(brownian_motion_two) 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def random_rectangle(max_side, min_side, sigma=0.5, ratio=1.0, coherce=True):

    assert min_side <= max_side

    #
    half_side = (max_side-min_side)/2
    center = max_side-half_side
    width  = random.normalvariate(0, sigma)*half_side
    height = random.normalvariate(0, sigma)*half_side

    #
    if ratio > 1:
        height = height/ratio
    else:
        width = width*ratio
   
    # Coherce value to max
    if coherce:
        width  = coherce_to(max_side, min_side, width+center)
        height = coherce_to(max_side, min_side, height+center)
    
    return width, height 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def n_rolls(count, n):
    """roll an n-sided die count times
    :type count: int
    :type n: int | str
    """
    if n in ('f', 'F'):
        return [random.randint(-1, 1) for _ in range(min(count, 100))]

    if count < 100:
        return [random.randint(1, n) for _ in range(count)]

    # Calculate a random sum approximated using a randomized normal variate with the midpoint used as the mu
    # and an approximated standard deviation based on variance as the sigma
    mid = .5 * (n + 1) * count
    var = (n ** 2 - 1) / 12
    adj_var = (var * count) ** 0.5

    return [int(random.normalvariate(mid, adj_var))] 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def test_single_source_storage_gaussian():
    n = 1000
    proc_1 = [rn.normalvariate(0, 1) for r in range(n)]  # correlated src
    proc_2 = [rn.normalvariate(0, 1) for r in range(n)]  # correlated src
    # Cast everything to numpy so the idtxl estimator understands it.
    data = Data(np.array([proc_1, proc_2]), dim_order='ps')
    settings = {
        'cmi_estimator': 'JidtKraskovCMI',
        'alpha_mi': 0.05,
        'tail_mi': 'one_bigger',
        'n_perm_max_stat': 21,
        'n_perm_min_stat': 21,
        'n_perm_mi': 21,
        'max_lag': 5,
        'tau': 1
        }
    processes = [1]
    network_analysis = ActiveInformationStorage()
    results = network_analysis.analyse_network(settings, data, processes)
    print('AIS for random normal data without memory (expected is NaN): '
          '{0}'.format(results._single_process[1].ais))
    assert results._single_process[1].ais is np.nan, (
        'Estimator did not return nan for memoryless data.') 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def testDependentGridSearchCallable(self):
        class Normal:
            def __call__(self, _config):
                return random.normalvariate(mu=0, sigma=1)

        class Single:
            def __call__(self, _config):
                return 20

        trials = self.generate_trials({
            "run": "PPO",
            "config": {
                "x": grid_search(
                    [tune.sample_from(Normal()),
                     tune.sample_from(Normal())]),
                "y": tune.sample_from(Single()),
            },
        }, "dependent_grid_search")
        trials = list(trials)
        self.assertEqual(len(trials), 2)
        self.assertEqual(trials[0].config["y"], 20)
        self.assertEqual(trials[1].config["y"], 20) 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def generate_tokens(mean, std_dev, num_tokens):
    tokens = {}
    cnt = 0
    while cnt < num_tokens:
        length = int(round(random.normalvariate(mean,
                                                std_dev)))
        if length < 2:
            continue
        flag = True
        while flag:
            new_token = ''.join(random.choice(string.ascii_lowercase)
                                for i in range(length))
            if tokens.get(new_token) is None:
                tokens[new_token] = True
                flag = False
        cnt += 1
    return list(tokens.keys()) 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def generate_table(mean, std_dev, tokens, num_records,
                   id_col_name, attr_col_name):
    records = []
    cnt = 0
    num_tokens = len(tokens)
    while cnt < num_records:
        size = int(round(random.normalvariate(mean,
                                              std_dev)))
        new_string = ''
        for i in range(size):
            rand = random.randint(0, num_tokens - 1)
            if i == 0:
                new_string += tokens[rand]
            else:
                new_string += ' ' + tokens[rand]

        records.append([cnt, new_string])
        cnt += 1
    return pd.DataFrame(records, columns=[id_col_name, attr_col_name]) 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def n_rolls(count, n):
    """roll an n-sided die count times
    :type count: int
    :type n: int | str
    """
    if n == "F":
        return [random.randint(-1, 1) for x in range(min(count, 100))]
    if n < 2:  # it's a coin
        if count < 100:
            return [random.randint(0, 1) for x in range(count)]
        else:  # fake it
            return [int(random.normalvariate(.5 * count, (.75 * count) ** .5))]
    else:
        if count < 100:
            return [random.randint(1, n) for x in range(count)]
        else:  # fake it
            return [int(random.normalvariate(.5 * (1 + n) * count,
                                             (((n + 1) * (2 * n + 1) / 6. -
                                               (.5 * (1 + n)) ** 2) * count) ** .5))] 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def coin(text, notice, action):
    """[amount] - flips [amount] coins
    :type text: str
    """

    if text:
        try:
            amount = int(text)
        except (ValueError, TypeError):
            notice("Invalid input '{}': not a number".format(text))
            return
    else:
        amount = 1

    if amount == 1:
        action("flips a coin and gets {}.".format(random.choice(["heads", "tails"])))
    elif amount == 0:
        action("makes a coin flipping motion")
    else:
        heads = int(random.normalvariate(.5 * amount, (.75 * amount) ** .5))
        tails = amount - heads
        action("flips {} coins and gets {} heads and {} tails.".format(amount, heads, tails)) 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def get_dist(d):
    return {
        'randrange': random.randrange, # start, stop, step
        'randint': random.randint, # a, b
        'random': random.random,
        'uniform': random, # a, b
        'triangular': random.triangular, # low, high, mode
        'beta': random.betavariate, # alpha, beta
        'expo': random.expovariate, # lambda
        'gamma': random.gammavariate, # alpha, beta
        'gauss': random.gauss, # mu, sigma
        'lognorm': random.lognormvariate, # mu, sigma
        'normal': random.normalvariate, # mu, sigma
        'vonmises': random.vonmisesvariate, # mu, kappa
        'pareto': random.paretovariate, # alpha
        'weibull': random.weibullvariate # alpha, beta
    }.get(d) 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def jump_diffusion_process(params: ModelParameters):
    """
    Produces a sequence of Jump Sizes which represent a jump
    diffusion process. These jumps are combined with a geometric brownian
    motion (log returns) to produce the Merton model.

    Arguments:
        params : ModelParameters
            The parameters for the stochastic model.

    Returns:
        jump sizes for each point in time (mostly zeroes if jumps are infrequent)
    """
    s_n = time = 0
    small_lamda = -(1.0 / params.lamda)
    jump_sizes = []
    for k in range(params.all_time):
        jump_sizes.append(0.0)
    while s_n < params.all_time:
        s_n += small_lamda * np.log(np.random.uniform(0, 1))
        for j in range(params.all_time):
            if time * params.all_delta <= s_n * params.all_delta <= (j + 1) * params.all_delta:
                jump_sizes[j] += random.normalvariate(params.jumps_mu, params.jumps_sigma)
                break
        time += 1
    return jump_sizes 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def get_correlated_geometric_brownian_motions(params: ModelParameters,
                                              correlation_matrix: np.array,
                                              n: int):
    """
    Constructs a basket of correlated asset paths using the Cholesky
    decomposition method.

    Arguments:
        params : ModelParameters
            The parameters for the stochastic model.
        correlation_matrix : np.array
            An n x n correlation matrix.
        n : int
            Number of assets (number of paths to return)

    Returns:
        n correlated log return geometric brownian motion processes
    """
    decomposition = sp.linalg.cholesky(correlation_matrix, lower=False)
    uncorrelated_paths = []
    sqrt_delta_sigma = np.sqrt(params.all_delta) * params.all_sigma
    # Construct uncorrelated paths to convert into correlated paths
    for i in range(params.all_time):
        uncorrelated_random_numbers = []
        for j in range(n):
            uncorrelated_random_numbers.append(random.normalvariate(0, sqrt_delta_sigma))
        uncorrelated_paths.append(np.array(uncorrelated_random_numbers))
    uncorrelated_matrix = np.asmatrix(uncorrelated_paths)
    correlated_matrix = uncorrelated_matrix * decomposition
    assert isinstance(correlated_matrix, np.matrix)
    # The rest of this method just extracts paths from the matrix
    extracted_paths = []
    for i in range(1, n + 1):
        extracted_paths.append([])
    for j in range(0, len(correlated_matrix) * n - n, n):
        for i in range(n):
            extracted_paths[i].append(correlated_matrix.item(j + i))
    return extracted_paths 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def initialize_v(n: int, k: int):
    """初始化交叉项

    :param n: 特征个数
    :param k: FM模型的度
    :return: 交叉项的系数权重
    """
    v = np.mat(np.zeros(shape=(n, k)))
    for i in range(n):
        for j in range(k):
            v[i, j] = normalvariate(0, 0.2)
    return v 

# 需要导入模块: import random [as 别名]
# 或者: from random import normalvariate [as 别名]
def random_resize(image, gt_image, lower_size, upper_size, sig):
    factor = random.normalvariate(1, sig)
    if factor < lower_size:
        factor = lower_size
    if factor > upper_size:
        factor = upper_size
    image = scipy.misc.imresize(image, factor)
    shape = gt_image.shape
    gt_zero = np.zeros([shape[0], shape[1], 1])
    gt_image = np.concatenate((gt_image, gt_zero), axis=2)
    gt_image = scipy.misc.imresize(gt_image, factor, interp='nearest')
    gt_image = gt_image[:, :, 0:2]/255
    return image, gt_image