Fixed number of replicates for Figure 4.

figure_4.py

@@ -2,13 +2,13 @@ import matplotlib.pyplot as plt
 import numpy as np
 from tqdm import tqdm
 
-from knn_regressors import passive_knn_regressor, passive_kernel_regressor, active_knn_regressor
+from knn_regressors import passive_knn_regressor, active_knn_regressor
 from SIR_simulation import Sampler
 
 plt.rc('font', size=22)  # Increase matplotlib default font size
 np.random.seed(0)  # Fix random seed for reproducibility
 
-num_replicates = 1**10 # Number of IID replications of experiment
+num_replicates = 2**10 # Number of IID replications of experiment
 ns = np.logspace(4, 12, 9, base=2., dtype=int)
 
 sim_len = 20
@@ -24,7 +24,7 @@ for replicate in tqdm(range(num_replicates)):
 
     # Simulated data for passive estimators
     Xs, Ys, Zs = sampler.generate_joint_samples(n)
-    passive_errors[replicate, n_idx] = passive_kernel_regressor(Xs, Ys, Zs, Xs_ground_truth, method='cv') - Ys_ground_truth
+    passive_errors[replicate, n_idx] = passive_knn_regressor(Xs, Ys, Zs, Xs_ground_truth, method='cv') - Ys_ground_truth
     
     active_estimates, Xs_active, Ys_active, Zs_active = active_knn_regressor(sampler, Xs_ground_truth, n, method='cv')
     active_errors[replicate, n_idx] = active_estimates - Ys_ground_truth