Beispiel #1
0
def main():
    theta = [2.4, 0.02, 0.2, 6.9]
    M0 = np.array([2.0, 5.0, 3.0])
    ds = abc.generate_dataset_full(hes1, theta)
    noisy_ds = abc.add_gaussian_noise_full(np.copy(ds))
    populations = (abc.smc(hes1, noisy_ds, [300.0]))
    sys.exit(0)
    plt.plot(t, p1, 'r-')
    plt.plot(t, p11, 'b-')
    plt.subplot(313)
    plt.plot(t, p2, 'r-')
    plt.plot(t, p21, 'b-')
    plt.show()
Beispiel #2
0
def main():
    theta = [2.4, 0.02, 0.2, 6.9 ]
    M0 = np.array([2.0, 5.0, 3.0])
    ds = abc.generate_dataset_full(hes1, theta)
    noisy_ds = abc.add_gaussian_noise_full(np.copy(ds))
    populations = (abc.smc(hes1, noisy_ds, [300.0]))
    sys.exit(0)
    plt.plot(t, p1,'r-')
    plt.plot(t, p11,'b-')
    plt.subplot(313)
    plt.plot(t, p2,'r-')
    plt.plot(t, p21,'b-')
    plt.show()
Beispiel #3
0
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()
Beispiel #4
0
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()