Feature/report _utils

ex1.py

@@ -10,8 +10,6 @@
 MATRIX_SIZE = 100
 
 P = 1  # population density
-L = 10
-
 
 class DoubtLevel(Enum):
     S1 = 1
@@ -88,7 +86,7 @@ def __init__(
             self,
             state,
             position,
-            cool_down_episode_countdown: int = L):
+            cool_down_episode_countdown):
         super().__init__(state=state.value, position=position)
         self._probability_to_believe = PROBABILITY_TO_BELIEVE[state]
         self._doubt_level = state
@@ -186,7 +184,9 @@ def __init__(
             n_cols: int,
             population_density: float,
             persons_distribution: Dict[DoubtLevel, float],
+            cool_down_l: int
     ):
+        self.cool_down_l = cool_down_l
         self.doubt_level_locations_dict = None
         self.persons_location = None
         self._n_rows = n_rows
@@ -267,7 +267,7 @@ def _init_matrix_cells(self, doubt_level_locations_dict: Dict[Tuple[int, int], D
             self._matrix[x][y] = PersonCell(
                 state=doubt_level,
                 position=Location(x=x, y=y),
-            )
+                cool_down_episode_countdown=self.cool_down_l)
         for row in range(self._n_rows):
             for col in range(self._n_cols):
                 if self._matrix[row][col] is None:

figure_utils.py

@@ -0,0 +1,48 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def plot_experiment(growth, believers, times=None, size=None):
+    if size is None:
+        size = len(growth)
+    plt.plot(np.arange(0, size), growth, label="average growth", color='blue', marker=".", markersize=5)
+    # plt.plot(np.arange(0, size), believers, label="average believers", color='olive', marker=".", markersize=5)
+
+    plt.legend()
+    plt.title(f"repeated experiment :={times}", fontsize=10)
+    plt.suptitle("Rumors statistics graph", fontsize=20)
+    plt.show()
+
+
+if __name__ == '__main__':
+    # TODO- growth is just a moq here.
+    average_growth = [193.33333333333337, 100.0, 79.24222320507151, 59.669774669774675, 46.970017230004544, 36.99652777777777, 32.82867310847102, 24.860085916992144, 22.81534676063843, 17.397486062081352, 17.903296309789695, 16.37275365511349, 14.575498594040559, 14.229832631131378, 13.864861961792778, 12.442980654101541, 11.61401134664488, 10.695049611295081, 10.993255817076077, 10.139359992140227, 9.678088656409814, 9.13223907374223, 8.843964121244094, 8.234181386854805, 7.806958718458997, 7.6010711912229185, 7.143834894222198, 6.488350853676337, 6.181228664919922, 5.538633638752102, 5.646309123757667, 5.458857949865897, 5.176192369404774, 4.849344557834169, 4.888544734178689, 4.651530571015955, 4.313374937785832, 4.075411434250982, 4.104679457964651, 4.083094010573979, 3.734984023960108, 3.23866410174052, 3.2558040355007765, 3.0098753122221003, 3.2181012410074428, 2.9256769734892156, 2.8769915831199135, 2.736147785161819, 2.8071389049394067, 2.678124345625458, 2.662740317059443, 2.907145425785121, 2.7636123429551325, 2.6764236709163107, 2.61919397319094, 2.468197600786283, 2.4995563050084058, 2.446386579981833, 2.1956674355768526, 2.003532666245132, 1.9085226484359037, 1.879645713528187, 1.7422203056475254, 1.8019004813549333, 1.762572468135476, 1.7394087242229042, 1.7366244360317193, 1.6441506073994117, 1.4110715801495795, 1.5103929035603745, 1.4629555708041255, 1.4212698666515677, 1.2241006313204332, 1.2258067881359038, 1.1201503086941527, 0.9389453293561542, 0.8432312591061075, 0.7085713859725408, 0.6438017145563447, 0.5944157908280138, 0.5443432758159367, 0.5777921281670665, 0.5383315024102504, 0.4517694832506945, 0.4488036891355387, 0.411116069702977, 0.45180971634783756, 0.43669754746713596, 0.44946367665472436, 0.42273460267714763, 0.38191647242119514, 0.3145598663946154, 0.23935893433239733, 0.23076393194186262, 0.21891568325636945, 0.2004315699861017, 0.1418870984088369, 0.07737074040434518, 0.0738354141495496, 0.0738354141495496]
+    # average_believers_per_turn
+    average_believers_per_turn = [0.00052, 0.0013399999999999998, 0.00268, 0.00455, 0.00704, 0.01003,
+                                  0.013519999999999999, 0.01736, 0.02201,
+                                  0.026970000000000004, 0.03252, 0.038470000000000004, 0.044649999999999995,
+                                  0.05136999999999999, 0.05887,
+                                  0.06677000000000001, 0.07504000000000001, 0.08354999999999999, 0.09246,
+                                  0.10127000000000001,
+                                  0.11062999999999998, 0.12023999999999999, 0.13036, 0.14139, 0.15233000000000002,
+                                  0.16376000000000002,
+                                  0.17584, 0.18788, 0.20019, 0.21336, 0.22606, 0.23893, 0.2519, 0.26515000000000005,
+                                  0.27843, 0.2913,
+                                  0.30483000000000005, 0.31804, 0.33131000000000005, 0.34450000000000003, 0.35773,
+                                  0.37124999999999997,
+                                  0.38476, 0.39785999999999994, 0.41176, 0.42595, 0.43986000000000003,
+                                  0.4529500000000001,
+                                  0.4663899999999999, 0.47969999999999996, 0.49367, 0.50809, 0.52227,
+                                  0.5368200000000001, 0.55128,
+                                  0.5659099999999999, 0.57995, 0.59457, 0.60962, 0.6247699999999999, 0.63929,
+                                  0.6537300000000001, 0.66783,
+                                  0.68256, 0.69709, 0.7115, 0.72567, 0.7389300000000001, 0.7516200000000001, 0.76493,
+                                  0.77789, 0.79091,
+                                  0.8038000000000001, 0.81587, 0.82774, 0.8390599999999999, 0.85025, 0.8609399999999999,
+                                  0.8703999999999998,
+                                  0.8796700000000002, 0.88918, 0.8979500000000001, 0.9065700000000001, 0.91519,
+                                  0.9228900000000001, 0.9301,
+                                  0.93743, 0.9446900000000001, 0.95137, 0.9575699999999999, 0.9638899999999999, 0.97008,
+                                  0.97589, 0.98079,
+                                  0.98486, 0.9887, 0.99153, 0.99375, 0.99526, 0.99656]
+    plot_experiment(growth=average_growth, believers=average_believers_per_turn, times=10)

report_utils.py

@@ -0,0 +1,104 @@
+from typing import Callable
+
+from ex1 import EnvMap, MATRIX_SIZE, P, PERSONS_DISTRIBUTION
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def calc_spread_rate(env_map: EnvMap):
+    count = 0
+    for i in range(env_map._n_rows):
+        for j in range(env_map._n_cols):
+            if env_map._matrix[i][j].did_hear_rumour_sometime():
+                count += 1
+    return count / len(env_map.persons_location)
+
+
+def run_experiment_multiple_times(env_map_creator: Callable[...,EnvMap], times):
+    raw_stats = []
+    for t in range(times):
+        believers = []
+        env_map = env_map_creator()
+        for i in range(100):
+            print(f"turn {i}==================")
+            env_map.spread_rumor()
+            believers.append(env_map.calculate_percentage_of_believeres())
+        raw_stats.append(believers)
+    return raw_stats
+
+
+def calc_growth(population):
+    growth = []
+    for pop in range(1, len(population)):
+        numbers = (population[pop] - population[pop - 1]) / population[pop - 1] * 100
+        growth.append(numbers)
+    return growth
+
+
+def calc_average_per_turn(raw_stats):
+    total_avgs = []
+    for turn_number in range(len(raw_stats[0])):
+        sum_for_turn = 0
+        for population in raw_stats:
+            sum_for_turn = sum_for_turn + population[turn_number]
+        total_avgs.append(sum_for_turn / len(raw_stats))
+    return total_avgs
+
+
+def raw_stats_to_growth(raw_stats):
+    growth = []
+    for population in raw_stats:
+        growth.append(calc_growth(population))
+    return growth
+
+
+
+
+def plot_experiment(graph, label: str, times=None,cool_down=None):
+    size = len(graph)
+
+    plt.plot(np.arange(0, size), graph, label=label, color='blue', marker=".", markersize=5)
+
+    plt.legend()
+    plt.title(f"repeated experiment :={times} cool_down:={cool_down}", fontsize=10)
+    plt.suptitle("Rumors statistics graph", fontsize=20)
+    plt.show()
+
+
+
+def main(env_map_creator: Callable[...,EnvMap],times=10) -> None:
+    raw_stats = run_experiment_multiple_times(env_map_creator, times)
+
+    for believers_percentage in raw_stats:
+        print(f"population belivers percentage:{believers_percentage}")
+    print()
+
+    avg_believers = calc_average_per_turn(raw_stats)
+    print(f"Average believers per turn:={avg_believers}")
+
+    growth_all = raw_stats_to_growth(raw_stats)
+    avg_growth = calc_average_per_turn(growth_all)
+
+    for cnt, growth in enumerate(growth_all):
+        print(f"turn {cnt} =============")
+        print(growth)
+        print()
+    print(f"Average growth per turn:={avg_growth}")
+
+    plot_experiment(avg_believers, label="average believers", times=times,cool_down=cool_down)
+    plot_experiment(avg_growth, label="average growth", times=times,cool_down=cool_down)
+
+def create_env_map(cool_down):
+    return EnvMap(
+        n_rows=MATRIX_SIZE,
+        n_cols=MATRIX_SIZE,
+        population_density=P,
+        persons_distribution=PERSONS_DISTRIBUTION,
+        cool_down_l=cool_down,
+    )
+
+
+if __name__ == "__main__":
+    for cool_down in [2,3,4,6,8,10,1]:
+        main(lambda: create_env_map(cool_down))
+