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()
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()
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()
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()
def lv_test():
theta = [1, 1]
ds = abc.generate_dataset(lv, theta)
ds = abc.add_gaussian_noise(ds)
population = abc.smc(lv, ds, [30.0, 16.0, 6.0, 5.0, 4.3])
sys.exit(0)
def lv_test():
theta = [1,1]
ds = abc.generate_dataset(lv, theta)
ds = abc.add_gaussian_noise(ds)
population = abc.smc(lv, ds, [30.0, 16.0, 6.0, 5.0, 4.3])
sys.exit(0)
