Python numpy.random.beta() Examples

The following are 7 code examples of numpy.random.beta(). 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.random , or try the search function .
Example #1
Source File: distributions.py    From particles with MIT License 5 votes vote down vote up 
def rvs(self, size=None):
        return random.beta(self.a, self.b, size=size)
Example #2
Source File: distributions.py    From particles with MIT License 5 votes vote down vote up 
def logpdf(self, x):
        return stats.beta.logpdf(x, self.a, self.b)
Example #3
Source File: distributions.py    From particles with MIT License 5 votes vote down vote up 
def ppf(self, x):
        return stats.beta.ppf(x, self.a, self.b)
Example #4
Source File: bmh.py    From python3_ios with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def plot_beta_hist(ax, a, b):
    ax.hist(beta(a, b, size=10000), histtype="stepfilled",
            bins=25, alpha=0.8, density=True)
Example #5
Source File: simulate_counts.py    From WASP with Apache License 2.0 5 votes vote down vote up 
def simulate_BNB(mean, sigma, n):
    # sys.stderr.write("%g %g %g\n" % (mean, sigma, n))
    mean_p = np.float64(n) / (n+mean)
    sigma = (1 / sigma)**2
    a = mean_p * (sigma)+1
    b = (1 - mean_p)*sigma
    
    p = beta(a, b)
    #sys.stderr.write("%f %f\n"%(n,p))
    counts = negative_binomial(n, p)
    return counts
Example #6
Source File: simulate_counts.py    From WASP with Apache License 2.0 5 votes vote down vote up 
def simulate_BB(tot, mean_p, sigma):
    a = mean_p * (1/sigma**2 - 1)
    b = (1-mean_p) * (1/sigma**2 - 1)

    p = beta(a, b)
    counts = binomial(tot, p)
    #sys.stderr.write("%f %f %i\n"%(mean_p,p,counts))
    return counts, (tot-counts)
Example #7
Source File: generate_data.py    From lifetimes with MIT License 4 votes vote down vote up 
def beta_geometric_beta_binom_model(N, alpha, beta, gamma, delta, size=1):
    """
    Generate artificial data according to the Beta-Geometric/Beta-Binomial
    Model.

    You may wonder why we can have frequency = n_periods, when frequency excludes their
    first order. When a customer purchases something, they are born, _and in the next
    period_ we start asking questions about their alive-ness. So really they customer has
    bought frequency + 1, and been observed for n_periods + 1

    Parameters
    ----------
    N: array_like
        Number of transaction opportunities for new customers.
    alpha, beta, gamma, delta: float
        Parameters in the model. See [1]_
    size: int, optional
        The number of customers to generate

    Returns
    -------
    DataFrame
        with index as customer_ids and the following columns:
        'frequency', 'recency', 'n_periods', 'lambda', 'p', 'alive', 'customer_id'

    References
    ----------
    .. [1] Fader, Peter S., Bruce G.S. Hardie, and Jen Shang (2010),
       "Customer-Base Analysis in a Discrete-Time Noncontractual Setting,"
       Marketing Science, 29 (6), 1086-1108.

    """

    if type(N) in [float, int, np.int64]:
        N = N * np.ones(size)
    else:
        N = np.asarray(N)

    probability_of_post_purchase_death = random.beta(a=alpha, b=beta, size=size)
    thetas = random.beta(a=gamma, b=delta, size=size)

    columns = ["frequency", "recency", "n_periods", "p", "theta", "alive", "customer_id"]
    df = pd.DataFrame(np.zeros((size, len(columns))), columns=columns)
    for i in range(size):
        p = probability_of_post_purchase_death[i]
        theta = thetas[i]

        # hacky until I can find something better
        current_t = 0
        alive = True
        times = []
        while current_t < N[i] and alive:
            alive = random.binomial(1, theta) == 0
            if alive and random.binomial(1, p) == 1:
                times.append(current_t)
            current_t += 1
        # adding in final death opportunity to agree with [1]
        if alive:
            alive = random.binomial(1, theta) == 0
        df.iloc[i] = len(times), times[-1] + 1 if len(times) != 0 else 0, N[i], p, theta, alive, i
    return df