class Ensemble_plot:
def __init__(self):
print("nyan")
def load_matrices(self, A, C, G):
self.A = A
self.C = C
self.G = G
self.ar = LinearStateSpace(A, C, G, mu_0=np.ones(4))
x, y = self.ar.simulate(ts_length=2000)
y = y.flatten()
self.__y_max = 1.3 * np.max(y)
self.__y_min = 1.3 * np.min(y)
def set_plot(self):
self.fig, self.ax = plt.subplots(figsize=(8, 5))
self.ax.set_ylim(self.__y_min, self.__y_max)
self.ax.set_xlabel("time", fontsize=16)
self.ax.set_ylabel("y_t", fontsize=16)
def calc_ensemble_mean(self):
self.ensemble_mean = np.zeros(T)
for i in range(I):
x, y = self.ar.simulate(ts_length=T)
y = y.flatten()
self.ax.plot(y, "c~", lw=0.8, alpha=0.5)
self.ensemble_mean = self.ensemble_mean + y
self.ensemble_mean = self.ensemble_mean / I
self.ax.plot(self.ensemble_mean,
color="b",
lw=2,
alpha=0.8,
label=r'$\bar y_t$')
def calc_moment(self):
print()
import numpy as np
import matplotlib.pyplot as plt
from quantecon import LinearStateSpace
from scipy.stats import norm
import random
phi_0, phi_1, phi_2 = 1.1, 0.8, -0.8
A = [[1, 0, 0], [phi_0, phi_1, phi_2], [0, 1, 0]]
C = np.ones((3, 1))
G = [0, 1, 0]
ar = LinearStateSpace(A, C, G, mu_0=np.ones(3))
x, y = ar.simulate(ts_length=50)
fig, ax = plt.subplots(figsize=(8, 4.6))
y = y.flatten()
print(y)
ax.plot(y, "-b", lw=2, alpha=0.7)
ax.grid()
ax.set_xlabel("time")
ax.set_ylabel("y")
plt.show()
##calc esemble_mean
esemble_mean = np.zeros(T)
I = 20
C = [sigma, 0, 0, 0]
G = [1, 0, 0, 0]
T0 = 10
T1 = 50
T2 = 75
T4 = 100
ar = LinearStateSpace(A, C, G, mu_0=np.ones(4))
ymin, ymax = -0.8, 1.25
fig, ax = plt.subplots(figsize=(8, 5))
ax.grid(alpha=0.4)
ax.set_ylim(ymin, ymax)
ax.set_ylabel(r'$y_t$', fontsize=16)
ax.vlines((T0, T1, T2), -1.5, 1.5)
ax.set_xticks((T0, T1, T2))
ax.set_xticklabels((r"$T$", r"$T'$", r"$T''$"), fontsize=14)
sample = []
for i in range(80):
rcolor = random.choice(('c', 'g', 'b'))
x, y = ar.simulate(ts_length=T4)
y = y.flatten()
ax.plot(y, color=rcolor, lw=0.8, alpha=0.5)
ax.plot((T0, T1, T2), (y[T0], y[T1], y[T2],), 'ko', alpha=0.5)
plt.show()
g = 10
n = 100
A = [[1, 0, 0],
[γ + g, ρ1, ρ2],
[0, 1, 0]]
G = [[γ + g, ρ1, ρ2], # this is Y_{t+1}
[γ, α, 0], # this is C_{t+1}
[0, β, -β]] # this is I_{t+1}
μ_0 = [1, 100, 100]
C = np.zeros((3,1))
C[1] = σ # stochastic
sam_t = LinearStateSpace(A, C, G, mu_0=μ_0)
x, y = sam_t.simulate(ts_length=n)
fig, axes = plt.subplots(3, 1, sharex=True, figsize=(12, 8))
titles = ['Output ($Y_t$)', 'Consumption ($C_t$)', 'Investment ($I_t$)']
colors = ['darkblue', 'red', 'purple']
for ax, series, title, color in zip(axes, y, titles, colors):
ax.plot(series, color=color)
ax.set(title=title, xlim=(0, n))
ax.grid()
axes[-1].set_xlabel('Iteration')
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from quantecon import Kalman
from quantecon import LinearStateSpace
from scipy.stats import norm
theta = 10
A, C, G, H = 1, 0, 1, 1
#mu_0は x0 の初期分布の平均パラメータ
ss = LinearStateSpace(A, C, G, H, mu_0=theta)
x_hat_0, Sigma_0 = 8, 1
kalman = Kalman(ss, x_hat_0, Sigma_0)
N = 5
x, y = ss.simulate(N)
y = y.flatten()
print(y)
#
fig, ax = plt.subplots(figsize=(10, 8))
xgrid = np.linspace(theta - 5, theta + 2, 200)
for i in range(N):
m, v = [float(z) for z in (kalman.x_hat, kalman.Sigma)]
print(m)
ax.plot(xgrid,
norm.pdf(xgrid, loc=m, scale=np.sqrt(v)),
label=r't={}'.format(i))
kalman.update(y[i])