示例#1
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def calculate_weight(prev_population, prev_weights, sim_theta):
    wsum = 0.0
    sigma = np.cov(np.vstack(prev_population).T)
    mean = utils.colMeans(np.vstack(prev_population))
    for particle, weight in izip(prev_population, prev_weights):
        wsum += weight * utils.dmvnorm(sim_theta, mean, sigma)
    return 0.1 / wsum
示例#2
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def calculate_weight(prev_population, prev_weights, sim_theta):
    wsum = 0.0
    sigma = np.cov(np.vstack(prev_population).T)
    mean = utils.colMeans(np.vstack(prev_population))
    for particle, weight in izip(prev_population, prev_weights):
        wsum += weight * utils.dmvnorm(sim_theta, mean, sigma)
    return 0.1 / wsum
示例#3
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def main():
    X0 = [1, 0.5]
    t = np.arange(0, 15, 0.1)
    ds = generate_data()
    populations = abc.smc(dx_dt, ds, [700.0])
    last_population = populations[:-1]
    theta = utils.colMeans(np.vstack(last_population))
    X = integrate.odeint(dx_dt, X0, t, args=(theta,))
    plt.plot(t,X)
    plt.show()
示例#4
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def main():
    X0 = [1, 0.5]
    t = np.arange(0, 15, 0.1)
    ds = generate_data()
    populations = abc.smc(dx_dt, ds, [700.0])
    last_population = populations[:-1]
    theta = utils.colMeans(np.vstack(last_population))
    X = integrate.odeint(dx_dt, X0, t, args=(theta, ))
    plt.plot(t, X)
    plt.show()
示例#5
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def gene_regulation():
    theta = [1., 10.]
    t = np.arange(0, 15, 0.1)
    X0 = 1.
    ds = abc.generate_dataset_full(gene_reg, theta)
    noisy_ds = abc.add_gaussian_noise_full(np.copy(ds))
    populations = abc.smc(gene_reg, noisy_ds, [300.0])
    theta1 = utils.colMeans(np.vstack(populations[:-1]))
    X = integrate.odeint(gene_reg, X0, t, args=(theta, ))
    plt.plot(t, X, 'r-')
    X1 = integrate.odeint(gene_reg, X0, t, args=(theta1, ))
    plt.plot(t, X1, 'b-')
    plt.plot(t, noisy_ds, 'go')
    plt.show()
示例#6
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def gene_regulation():
    theta = [1., 10.]
    t = np.arange(0, 15, 0.1)
    X0 = 1.
    ds = abc.generate_dataset_full(gene_reg, theta)
    noisy_ds = abc.add_gaussian_noise_full(np.copy(ds))
    populations = abc.smc(gene_reg, noisy_ds, [300.0])
    theta1 = utils.colMeans(np.vstack(populations[:-1]))
    X = integrate.odeint(gene_reg, X0, t, args=(theta, ))
    plt.plot(t, X, 'r-')
    X1 = integrate.odeint(gene_reg, X0, t, args=(theta1, ))
    plt.plot(t, X1, 'b-')
    plt.plot(t, noisy_ds, 'go')
    plt.show()
示例#7
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def plot_solution(population, ds):
    ti = [t / 10 for t in times]
    theta1 = np.array([1, 1])
    plt.figure(1)
    theta = utils.colMeans(np.vstack(population))
    X0 = np.array([1, 0.5])
    t = np.arange(0, 15, 0.1)
    X = integrate.odeint(dx_dt, X0, t, args=(theta, ))
    Y = integrate.odeint(dx_dt, X0, t, args=(theta1, ))
    x, y = X.T
    x1, y1 = Y.T
    plt.figure(3)
    plt.subplot(211)
    plt.plot(t, x, 'r-', label='x(t)')
    plt.plot(t, x1, 'g-', label='x(t))')
    plt.plot(ti, ds[:, 0], marker='s', linestyle='', color='g')
    plt.subplot(212)
    plt.plot(t, y, 'b-', label='y(t)')
    plt.plot(t, y1, 'g-', label='y(t)')
    plt.plot(ti, ds[:, 1], marker='^', linestyle='', color='g')
    plt.xlabel('time')
    plt.show()
示例#8
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def plot_solution(population, ds):
    ti = [t/10 for t in times]
    theta1 = np.array([1,1])
    plt.figure(1)
    theta = utils.colMeans(np.vstack(population))
    X0 = np.array([1, 0.5])
    t = np.arange(0, 15, 0.1)
    X= integrate.odeint(dx_dt, X0, t, args=(theta,))
    Y= integrate.odeint(dx_dt, X0, t, args=(theta1,))
    x,y = X.T
    x1,y1 = Y.T
    plt.figure(3)
    plt.subplot(211)
    plt.plot(t, x, 'r-', label='x(t)')
    plt.plot(t, x1,'g-',label='x(t))')
    plt.plot(ti, ds[:, 0], marker='s', linestyle='', color='g')
    plt.subplot(212)
    plt.plot(t, y, 'b-', label='y(t)')
    plt.plot(t, y1, 'g-', label='y(t)')
    plt.plot(ti, ds[:, 1], marker='^', linestyle='', color='g')
    plt.xlabel('time')
    plt.show()