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import numpy as np
import matplotlib.pyplot as plt
from scipy.stats.distributions import binom
def binomial(n, k, p):
return binom.pmf(k, n, p)
def get_uninformative_prior(K_min, K_max, num_K):
P_k = np.ones(num_K)/num_K
N = np.linspace(K_min, K_max, num_K)
return P_k, N
class FieldStudy(object):
def __init__(self, true_N, K_min, K_max, num_K):
self.true_N = true_N
self.marked = np.repeat(False, self.true_N)
# parameters related to the population domain
self.K_min = K_min
self.K_max = K_max
self.num_K = num_K
self.P_k, self.N = get_uninformative_prior(self.K_min, self.K_max, self.num_K)
self.C = [] # Sampled
self.R = [] # Recaptured
self.M = [] # Marked at start of trial
def update_dist(self, marked, captured, recaptured):
# Compute the unnormalized distribution
unnormalized_P_k = self.P_k * binomial(self.C[-1], self.R[-1], self.M[-1] / self.N)
# Update the prior to trim off values that are impossible now
unnormalized_P_k[self.N < self.M[-1]] = 0
self.P_k = unnormalized_P_k/np.sum(unnormalized_P_k)
def sample(self, difficulty_of_catch=0.05):
captured = np.random.rand(true_N) < difficulty_of_catch
recaptured = self.marked & captured
self.C.append(np.sum(captured)) # how many were caught
self.R.append(np.sum(recaptured)) # how many caught were already marked
self.marked |= captured # update which were marked
self.update_dist(self.M[-1], self.C[-1], self.R[-1])
return captured
# Example usage
true_N = 8000
fs = FieldStudy(true_N, 100, 100000, 1000)
for _ in range(21):
print('M', fs.M)
print('C', fs.C)
print('R', fs.R)
print("Mode", fs.N[np.argmax(fs.P_k)])
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