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
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
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()
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()
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 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()
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()