added exam ex10

exam/ex10/main.py

@@ -0,0 +1,64 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+class StochastikIterator(object):
+	def __init__(self, r, num_simulations, chi0, std_deviation, maxiter=None):
+		self._r = r
+		self._num_simulations = num_simulations
+		self._chi0 = chi0
+		self._std_deviation = std_deviation
+		self._maxiter=maxiter
+		self._chi = None
+		self._currentiter = None
+
+	def __iter__(self):
+		self._currentiter = 0
+		self._chi = np.array([self._chi0] * self._num_simulations, dtype=np.float64)
+		return self
+
+	def __next__(self):
+		if(self._maxiter and self._currentiter == self._maxiter):
+			raise StopIteration()
+		self._currentiter += 1
+
+
+		result = self._chi.copy()
+		self._chi *= np.exp(r - np.random.normal(scale=self._std_deviation, size=self._num_simulations))
+
+		return result
+
+
+
+	def do(self, iters=None):
+		if(not (iters or self._maxiter)):
+			raise ValueError("iters or maxiter must be specified")
+
+		if(iters):
+			until = iters
+		if(self._maxiter and (not iters or (iters and self._maxiter < iters))):
+				until = self._maxiter
+
+
+		self._currentiter = 0
+		self._chi = np.array([self._chi0] * self._num_simulations, dtype=np.float64)
+		for i in range(until):
+			self._chi *= np.exp(r - np.random.normal(scale=self._std_deviation, size=self._num_simulations))
+
+		return self._chi.copy()
+
+
+
+mean_chi = lambda r: np.mean(StochastikIterator(r, 500, 100, 0.25).do(70))
+
+for r in [0.05, 0, -0.05]:
+	print("r =", r, "\t<chi> = ", mean_chi(r))
+
+
+R = np.arange(-4, 4, 0.01)
+
+M = [mean_chi(r) for r in R]
+
+plt.plot(R, M, label="$<\\chi>$")
+plt.show()
+
+