import tensorflow as tf
import numpy as np
from sklearn.utils import shuffle
from sklearn.preprocessing import MinMaxScaler
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import categorical_crossentropy
%matplotlib
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import itertools
import matplotlib.pyplot as plt
import os.path
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Input, Lambda, Dense, Flatten,Conv2D
from tensorflow.keras.models import Model
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img
from tensorflow.keras.models import Sequential
import numpy as np
from glob import glob
import matplotlib.pyplot as plt
from tensorflow.keras.layers import MaxPooling2D
Using matplotlib backend: <object object at 0x7f2cbcedcdc0>
import numpy as np
from enum import Enum
from random import random, randint, choice
from collections import defaultdict
from typing import List, Callable, Tuple
from copy import deepcopy
# Generate Gridworld Function
def gen_gridworld(dim:int, prob:float, solvable: bool = None, is_terrain_enabled: bool = True, source: Tuple[int, int] = None, target: Tuple[int, int] = None) -> (List[List[float]], Tuple[int, int]):
"""generate gridworld implemenation
generates a dim by dim gridworld where the (0,0) is source
every cell besides the source cell has probability of "prob" to be blocked
if a cell is not blocked then it is randomly assigned a terrain type out of flat, hilly, and forest
at the end a random not blocked cell will be chosen as the target
NOTE: take of note of what is stored at each cell. The first value indicates what cell type it is and the second value indicates if the cell contains the target
Args:
dim (int) : the dimensions of the square grid
prob (float): the probability of a square being blocked
solvability (bool): returns a grid world that is either solvable or unsolvable depending on this param
Returns:
list[list[int]]: a dim by dim square grid with blocked and unblock squares
Tuple[int, int]: a tuple containing the coordinates of target cell
"""
grid = np.array([[0.0] * dim for _ in range(dim)])
while True:
for i in range(dim):
for j in range(dim):
p = random()
if p >= prob:
if is_terrain_enabled:
t = randint(1,3)
if t == 1:
grid[i][j] = 0.8 # Terrain.FLAT
elif t == 2:
grid[i][j] = 0.5 # Terrain.HILLY
elif t == 3:
grid[i][j] = 0.2 # Terrain.FOREST
else:
grid[i][j] = 0.0
else:
grid[i][j] = 1.0 # blocked cell
# Place the source and target
if source:
grid[source[0]][source[1]] = 0.0
if target:
grid[target[0]][target[1]] = 0.0
unblocked = [(i,j) for i in range(dim) for j in range(dim) if grid[i][j] != 1.0]
# no unblocked cells
if not unblocked:
continue
# choose q random source and target, note source and target can be the same cell
if not source:
source = choice(unblocked)
if not target:
source = choice(unblocked)
# pretty_print_explored_region(grid)
path, *_ = a_star(grid, source, target)
# print(path)
if solvable is None or solvable == bool(path):
break
return grid, source, target
# Heuristics
def manhattan_heuristic(x1: int, y1: int, x2: int, y2: int):
return abs(x1 - x2) + abs(y1 - y2)
# A*
from collections import defaultdict
import heapq
from typing import Tuple, Callable, List, Dict
import numpy as np
def a_star(
grid: List[List[float]],
source: Tuple[int, int] = (0, 0),
goal: Tuple[int, int] = None,
h: Callable = manhattan_heuristic,
W: int = 1,
) -> (List[Tuple[int, int]], int, List[Tuple[int,int]], int, int):
"""A* Implementation
Used to find optimal shortest path from source to goal on a full gridworld (i.e with complete information of the block cells).
Args:
grid (2d int list): A square grid with 0 representing empty square and 1 representing a blocked square
source (tuple) Optional: control where to start the A* search from
goal (tuple): control where the target of the A* search algorithm
h (func) Optional: heuristic function and the default heuristic function is the manhattan distance
W (int) Optional: weight on the heuristic function
Returns:
list(tuple(int, int)): the path from the source node to the goal node which consists of coordinate points (i.e. x,y) in the form of tuples.
int: the number of steps that need to taken from soure to goal (i.e. len(path) - 1 or 2(dim - 1))
list(tuple(int,int)): all visited squares from the source point. Contains int tuples in the form of (x,y).
int: trajectory - num of times a node was popped from the fringe
int: cells processed - num of times we check if a particular grid square was block or not
"""
if source == goal:
return [source], 0, [source], 0, 0
g = defaultdict(lambda: float("inf")) # dictionary with key: (x,y) tuple and value: length of the shortest path from (0,0) to (x,y)
g[source] = 0 # set distance to source 0
parent = {
source: None
} # dictionary with key: (x,y) tuple and value is a (x,y) tuple where the value point is the parent point of the key point in terms of path traversal
pq = [(0, -g[source], source)]
trajectory = cells_processed = 0 # statistics to keep track while A* runs
while pq:
_, _, curr = heapq.heappop(pq) # pop from priority queue
# pretty_print_explored_region(grid, list(g.keys())) # uncomment if you want to see each step of A*
trajectory += 1
if curr == goal: # check if we found our goal point
break
# generate children
for dx, dy in [(0, 1), (-1, 0), (0, -1), (1, 0)]: # right down left up
new_x, new_y = (curr[0] + dx, curr[1] + dy) # compute children
# skip out of bounds or blocked children
if not (0 <= new_x < len(grid) and 0 <= new_y < len(grid)) or grid[new_x][new_y] >= 1.0:
continue
children = (new_x, new_y)
new_g= g[curr] + 1 # add + 1 to the real cost of children
if (
children not in g or new_g < g[children]
): # only care about new undiscovered children or children with lower cost
cells_processed += 1
g[children] = new_g # update cost
parent[children] = curr # update parent of children to current
h_value = h(*children, *goal) # calculate h value
f = new_g + W * h_value # generate f(n')
heapq.heappush(
pq, (f, -new_g, children)
) # add children to priority queue | -new_g is added to break ties
else:
return [], float("inf"), list(g.keys()), trajectory, cells_processed
# generate path traversal using parent dict
path = [curr]
while curr != source:
curr = parent[curr]
path.append(curr)
path.reverse() # reverse the path so that it is the right order
return path, g[goal], list(g.keys()), trajectory, cells_processed
# Nicely prints out the grid
# Testing use only.
class colors: #
PINK = '\033[95m'
RED = '\033[91m'
BLUE = '\033[94m'
BOLD = '\033[1m'
ENDC = '\033[0m'
def pretty_print_explored_region(grid, visited=[], path=[]):
color = '\33[102m'
grid_copy = deepcopy(grid.tolist())
for x,y in visited:
grid_copy[x][y] = colors.RED + str(grid[x][y]) + colors.ENDC
for x,y in path:
if type(grid_copy[x][y]) == str:
grid_copy[x][y] = colors.PINK + str(grid[x][y]) + colors.ENDC
else:
grid_copy[x][y] = colors.BLUE + str(grid[x][y]) + colors.ENDC
for i in range(len(grid)):
for j in range(len(grid)):
if type(grid_copy[i][j]) != str:
if grid_copy[i][j] == 0.8:
color = '\33[43m'
elif grid_copy[i][j] == 0.5:
color = '\33[102m'
elif grid_copy[i][j] == 0.2:
color = '\33[42m'
elif grid_copy[i][j] == 1:
color = '\33[100m'
elif grid_copy[i][j] == 0:
color = '\033[94m'
elif grid_copy[i][j] == 2.0:
color = '\033[104m'
grid_copy[i][j] = color + str(grid_copy[i][j]) + colors.ENDC
for row in grid_copy:
for col in row:
print(col, end=" ") # print each element separated by space
print() # Add newline
print("-------")
# RED == visited
# Purple == path that was also visited
# Blue == path but was not visited (i.e. if agent knew the complete grid then it was have used the blue path)
class CardinalDirection(Enum):
NORTH = 0
EAST = 1
SOUTH = 2
WEST = 3
def __int__(self):
return self.value
def _get_cardinal_direction(x1, y1, x2, y2):
if x1 > x2:
return CardinalDirection.NORTH
elif x1 < x2:
return CardinalDirection.SOUTH
elif y1 > y2:
return CardinalDirection.WEST
elif y1 < y2:
return CardinalDirection.EAST
else:
raise Exception("Invalid cardinal direction")
import numpy as np
### Repeated A* - Remastered for P4
def repeated_a_star(complete_grid: List[List[int]], use_full_grid=False, h:Callable = manhattan_heuristic, source:Tuple[int,int] = (0,0), goal:Tuple[int,int] = None, W:int = 1) -> (List[List[int]], List[Tuple[int,int]], int, int, int, int):
"""Repeated A* Implementation
Used to find optimal shortest path from source to goal on a full gridworld (i.e with complete information of the block cells).
Args:
complete_grid (2d int list): A square grid with 0 representing empty square and 1 representing a blocked square
user_full_grid (bool) Optinal: False indicates that the agent should not be able to see the whole grid and True represents the agent has information of the entire grid
h (func) Optional: heuristic function and the default heuristic function is the manhattan distance
source (tuple) Optional: can control where to start the A* search from
goal (tuple) Optional: can control where A* star tries to explore to
Returns:
list[list[int]]: the discovered grid after running repeated A*
list[tuple[int, int]]: the path from the source node to the goal node which consists of coordinate points (i.e. x,y) in the form of tuples.
int: the number of steps in the path from source to goal (i.e. len(path) - 1 or 2(dim - 1))
list[tuple[int,int]]: all visited squares from the source point. Contains int tuples in the form of (x,y).
int: trajectory
int: num of cells processed
int: number of repeats in the repeated A* algorithm
return grid, path_discovered_grid, len(path_discovered_grid)-1, list(g.keys()), trajectory, cells_processed, repeats
"""
original_source = source #we need to remember the original_source node bc in repeated A* source changes
n, m = len(complete_grid) - 1, len(complete_grid[0]) - 1 # dimensions of grid
goal = (n, m) # goal coord
trajectory = cells_processed = repeats = 0 #statistics that is tracked during function call
safe = set()
if use_full_grid: # if agent is allowed knowledge of full grid
grid = np.array(deepcopy(complete_grid))
else: # if agent has to discover
grid = np.array([[0.5]*(m+1) for _ in range(n+1)])
grid[source[0]][source[1]] = 0.0
grid_states = []
actions = []
while source != goal: # execution step of repeated A*
repeats += 1
path, *_ = a_star(grid, source, goal)
if not path: # no path was found from original source to goal
return grid, [], grid_states, actions, 0, list(safe), trajectory, cells_processed, repeats
# iterator = iter(path)
# next(iterator)
for pos in path: # travel along the planned path
x,y = pos
if source != pos:
source_neighbors = [source[0], source[1]]
for dx, dy in [(0, 1), (-1, 0), (0, -1), (1, 0)]: # generate children
new_x, new_y = (source[0] + dx, source[1] + dy)
if 0 <= new_x <= n and 0 <= new_y <= m:
source_neighbors.extend([new_x, new_y, grid[new_x][new_y]])
else:
source_neighbors.extend([0,0,1])
grid_states.append([np.array(deepcopy(grid)), source_neighbors])
actions.append(_get_cardinal_direction(*source, *pos))
grid[x][y] = complete_grid[x][y]
if grid[x][y] == 1.0:
break
safe.add(pos) # add to closed set
trajectory += 1
source = pos
# uncomment if we want agent to see in all 4 directions
# for dx, dy in [(0, 1), (-1, 0), (0, -1), (1, 0)]: # generate children
# new_x, new_y = [x + dx, y + dy]soru
# if 0 <= new_x <= n and 0 <= new_y <= m:
# grid[new_x][new_y] = complete_grid[new_x][new_y] # update knowledge of the grid world
# pretty_print_explored_region(grid, list(safe))
if not use_full_grid:
for i in range(len(grid)):
for j in range(len(grid[0])):
if not (i,j) in safe and grid[i][j] != 1.0:
grid[i][j] = 2.0
path_discovered_grid, length_discovered_grid, visited_discovered_grid, _, _ = a_star(grid, original_source, target) # call regular A* to generate path from source to goal in final discovered grid world return grid, path_discovered_grid, grid_states, actions, len(path_discovered_grid)-1, list(safe), trajectory, cells_processed, repeats
return grid, path_discovered_grid, grid_states, actions, len(path_discovered_grid) - 1, list(safe), trajectory, cells_processed, repeats
NUM_TRAINING_MAZES = 100
NUM_TESTING_MAZES = 100
MAZE_DIM = 50
DENSITY = 0.3
SOLVABILITY = True
IS_TERRAIN_ENABLED = False
source = (0,0)
target = (MAZE_DIM-1, MAZE_DIM-1)
# grid, _, _ = gen_gridworld(dim, density, solvability, is_terrain_enabled, source, target)
# # pretty_print_explored_region(grid)
# explored_grid, path, grid_states, actions, len_path, visited_cells, *_ = repeated_a_star(grid, True)
# pretty_print_explored_region(explored_grid, visited_cells, path)
# explored_grid, path, grid_states, actions, len_path, visited_cells, *_ = repeated_a_star(grid)
# pretty_print_explored_region(explored_grid, visited_cells, path)
# print(len(grid_states))
# print(len(actions))
def gen_grid_data(num_iter: int, data_format: int):
all_grids = []
all_sources = []
all_actions = []
for i in range(num_iter):
if i % 100 == 0:
print("Maze", i)
grid, _, _ = gen_gridworld(MAZE_DIM, DENSITY, SOLVABILITY, IS_TERRAIN_ENABLED, source, target)
explored_grid, path, grid_states, actions, len_path, visited_cells, *_ = agent(grid, dataformat)
grids, sources = zip(*grid_states)
all_grids.extend(grids)
all_sources.extend(sources)
all_actions.extend(actions)
return (np.array(all_grids), np.array(all_sources), np.array(all_actions))
def generate_confusion_matrix( data, labels ):
mat = [ [ 0 for i in range(10) ] for j in range(10) ]
out = model.predict(data)
predictions = np.argmax( out , axis = 1 )
for i in range( data.shape[0] ):
mat[ labels[i] ][ predictions[i] ] += 1
for i in range(10):
print( "\t".join( [ str(c) for c in mat[i] ] ) )
# Full Dense Layer for P1's Agent
# Model Creation
# NUM_PROCESSES = 10
# PROCESS_DATA_LIMIT = 100
# BATCH_SIZE = 100
# NUM_TRAINING_MAZES = 1000 * NUM_PROCESSES
# NUM_TESTING_MAZES = 100
# MAZE_DIM = 50
# DENSITY = 0.3
# SOLVABILITY = True
# IS_TERRAIN_ENABLED = False
# source = (0,0)
# target = (MAZE_DIM-1, MAZE_DIM-1)
# total_grid_states = []
# total_actions = []
# total_test_grid_states = []
# total_test_grid_actions = []
# def gen_grid_data(idx, num_iter):
# all_grids = []
# all_sources = []
# all_actions = []
# for i in range(num_iter):
# if i % 100 == 0:
# print("Maze", i)
# grid, _, _ = gen_gridworld(MAZE_DIM, DENSITY, SOLVABILITY, IS_TERRAIN_ENABLED, source, target)
# explored_grid, path, grid_states, actions, len_path, visited_cells, *_ = repeated_a_star(grid)
# grids, sources = zip(*grid_states)
# all_grids.extend(grids)
# all_sources.extend(sources)
# all_actions.extend(actions)
# return (np.array(all_grids), np.array(all_sources), np.array(all_actions))
# def gen_grid_data_train(idx=0):
# return gen_grid_data(idx, 250)
# def gen_grid_data_test(idx=0):
# return gen_grid_data(idx, PROCESS_DATA_LIMIT)
# arrs = [gen_grid_data_test()]
# total_test_grids = np.array([x for sublist in arrs for x in sublist[0]])
# total_test_sources = np.array([x for sublist in arrs for x in sublist[1]])
# total_test_actions = np.array([x for sublist in arrs for x in sublist[2]])
### BLOCK 4 ###
# import tensorflow as tf
# # To Enable GPU
# # NOTE: Deepnote has no GPU available for free tier
# physical_devices = tf.config.experimental.list_physical_devices('GPU')
# print("NUM GPUs Available: ", len(physical_devices))
# tf.config.experimental.set_memory_growth(physical_devices[0], True)
# def generate_confusion_matrix( data, labels ):
# mat = [ [ 0 for i in range(4) ] for j in range(4) ]
# print(np.shape(data[0]))
# print(np.shape(data[1]))
# out = model.predict(data)
# predictions = np.argmax( out , axis = 1 )
# unique, counts = np.unique(predictions, return_counts=True)
# print("predict", dict(zip(unique, counts)))
# unique, counts = np.unique(np.array([int(x) for x in labels]), return_counts=True)
# print("labels", dict(zip(unique, counts)))
# for i in range( data[0].shape[0] ):
# mat[ int(labels[i]) ][ predictions[i] ] += 1
# for i in range(4):
# print( "\t".join( [ str(c) for c in mat[i] ] ) )
# grids_train = np.reshape(total_grids, (-1, MAZE_DIM, MAZE_DIM))
# sources_train = total_sources
# output_train = tf.keras.utils.to_categorical(
# np.array([int(x) for x in total_actions]),
# 4,
# )
# grids_test = np.array([x[0] for x in total_test_grid_states])
# grids_test = np.reshape(total_test_grids, (-1, MAZE_DIM, MAZE_DIM) )
# sources_test = total_test_sources
# output_test = np.array([int(x) for x in total_test_actions])
# print("bottle 3")
# Grid input
# Models
def generate_dense_model():
grid_input = tf.keras.layers.Input( shape = (MAZE_DIM, MAZE_DIM) )
flatten_grids = tf.keras.layers.Flatten()( grid_input )
# Source input
source_input = tf.keras.layers.Input( shape = (14) )
# Full input
grids_and_sources = tf.keras.layers.Concatenate()([flatten_grids, source_input])
dense_1 = tf.keras.layers.Dense( units = 2000, activation = tf.nn.relu )( grids_and_sources )
dense_2 = tf.keras.layers.Dense( units = 500, activation = tf.nn.relu )( dense_1 )
probabilities = tf.keras.layers.Dense( units = 4, activation = tf.nn.softmax )( dense_2 )
model = tf.keras.Model(inputs=[grid_input, source_input], outputs=probabilities)
model.compile( optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'] )
print(model.summary())
return model
def generate_cnn_model():
# Grid input
grid_input = tf.keras.layers.Input( shape = (MAZE_DIM, MAZE_DIM) )
flatten_grids = tf.keras.layers.Flatten()( grid_input )
# Source input
source_input = tf.keras.layers.Input( shape = (14) )
# Full input
grids_and_sources = tf.keras.layers.Concatenate()([flatten_grids, source_input])
grids_and_sources = tf.expand_dims(grids_and_sources, axis = -1)
grids_and_sources = tf.expand_dims(grids_and_sources, axis = -1)
conv2d_1 = tf.keras.layers.Conv2D(filters=256,kernel_size=2,padding="same",activation="relu")( grids_and_sources )
maxpool2d_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_1 )
conv2d_2 = tf.keras.layers.Conv2D(filters=32,kernel_size=2,padding="same",activation="relu")( maxpool2d_1 )
maxpool2d_2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_2 )
conv2d_3 = tf.keras.layers.Conv2D(filters=4,kernel_size=2,padding="same",activation="relu")( maxpool2d_2 )
maxpool2d_3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_3 )
flatten = tf.keras.layers.Flatten()( maxpool2d_3 )
dense_1 = tf.keras.layers.Dense( units = 500, activation = tf.nn.relu )( flatten )
logits = tf.keras.layers.Dense( units = 4, activation = "softmax" )( dense_1 )
model = tf.keras.Model(inputs=[grid_input, source_input], outputs=logits)
model.compile( optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'] )
print(model.summary())
return model
# model = generate_cnn_model()
# print("bottle 5")
# model = tf.keras.models.load_model('./test_data/')
# generate_confusion_matrix( [grids_test, sources_test], output_test )
# NUM_PROCESSES = 10
# PROCESS_DATA_LIMIT = 100
# BATCH_SIZE = 250
# from sklearn.utils import class_weight
# NUM_TRAINING_MAZES = 1000
# total_grids = []
# total_sources = []
# total_actions = []
# for i in range(0, NUM_TRAINING_MAZES, BATCH_SIZE):
# if i != 0:
# model = tf.keras.models.load_model('./test_data/')
# print(f"Training starting on maze {i}")
# total_grids, total_sources, total_actions = gen_grid_data_train()
# grids_train = np.reshape(total_grids, (-1, MAZE_DIM, MAZE_DIM))
# sources_train = total_sources
# output_train = tf.keras.utils.to_categorical(
# np.array([int(x) for x in total_actions]),
# 4,
# )
# # y_ints = [y.argmax() for y in output_train]
# # class_weights = dict(enumerate(class_weight.compute_class_weight('balanced', classes=np.unique(y_ints), y=y_ints)))
# north_multiplier = 3
# west_multiplier = 2
# class_weights = {
# 0 : north_multiplier,
# 1 : 1,
# 2 : 1,
# 3 : west_multiplier,
# }
# # multiplier = 0 if multiplier == 0 else multiplier - 1
# history = model.fit( [grids_train, sources_train], output_train, epochs = 10, validation_split=.1)
# model.save('./test_data/')
# generate_confusion_matrix( [grids_test, sources_test], output_test )
# model.save('./test_data/')
# Dataset will be of shape (# of inputs, # rows, # cols)
# CNN for P1's Agent
def p1_cnn_model():
# Grid input
grid_input = tf.keras.layers.Input( shape = (MAZE_DIM, MAZE_DIM) )
flatten_grids = tf.keras.layers.Flatten()( grid_input )
# Source input
source_input = tf.keras.layers.Input( shape = (2) )
# Full input
grids_and_sources = tf.keras.layers.Concatenate()([flatten_grids, source_input])
#Conv2d layer requires minimum ndim of 4, so adding 2 more empty dims
grids_and_sources = tf.expand_dims(grids_and_sources, axis = -1)
grids_and_sources = tf.expand_dims(grids_and_sources, axis = -1)
#Model
conv2d_1 = tf.keras.layers.Conv2D(filters=256,kernel_size=2,padding="same",activation="relu")( grids_and_sources )
maxpool2d_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_1 )
conv2d_2 = tf.keras.layers.Conv2D(filters=32,kernel_size=2,padding="same",activation="relu")( maxpool2d_1 )
maxpool2d_2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_2 )
conv2d_3 = tf.keras.layers.Conv2D(filters=4,kernel_size=2,padding="same",activation="relu")( maxpool2d_2 )
maxpool2d_3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same')( conv2d_3 )
flatten = tf.keras.layers.Flatten()( maxpool2d_3 )
dense_1 = tf.keras.layers.Dense( units = 500, activation = tf.nn.relu )( flatten )
logits = tf.keras.layers.Dense( units = 4, activation = "softmax" )( dense_1 )
model = tf.keras.Model(inputs=[grid_input, source_input], outputs=logits)
model.compile( optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'] )
print(model.summary())
return model
# # Data
# nn_path_data =[
# [172, 171, 161, 143, 150, 146, 125, 127, 176, 311, 137, 148, 179, 316, 143, 157, 221, 152, 213, 184, 156, 225, 148, 191, 173, 216, 158, 168, 164, 127, 167, 191, 174, 179, 171, 160, 194, 163, 154, 137, 165, 147, 179, 208, 181, 157, 199, 145, 168, 153, 188, 145, 183, 126, 135, 155, 189, 239, 164, 214, 152, 155, 160, 397, 177, 173, 145, 134, 157, 151, 171, 144, 219, 162, 224, 199, 155, 154, 156, 192, 183, 166, 157, 188, 166, 150, 177, 157, 162, 146, 155, 206, 133, 123, 162, 160, 157, 175, 176, 171],
# [167, 156, 131, 212, 186, 129, 151, 179, 154, 136, 160, 172, 194, 128, 118, 163, 246, 129, 162, 136, 191, 144, 140, 191, 150, 148, 159, 163, 192, 141, 263, 149, 171, 139, 175, 189, 173, 129, 150, 137, 163, 182, 184, 127, 164, 114, 202, 171, 132, 141, 150, 190, 125, 172, 180, 200, 193, 138, 130, 142, 187, 173, 159, 146, 200, 193, 172, 127, 142, 167, 193, 198, 190, 163, 169, 143, 196, 198, 224, 276, 149, 174, 157, 141, 160, 175, 162, 134, 125, 211, 115, 152, 143, 233, 276, 184, 157, 155, 160, 136],
# [143, 206, 150, 156, 189, 126, 149, 139, 158, 130, 160, 163, 203, 157, 139, 154, 201, 198, 220, 156, 186, 157, 127, 180, 178, 135, 176, 160, 180, 159, 173, 172, 180, 132, 184, 165, 133, 217, 227, 152, 189, 239, 166, 154, 163, 283, 142, 188, 151, 248, 152, 153, 163, 132, 147, 152, 221, 149, 165, 186, 151, 139, 193, 184, 181, 175, 210, 144, 139, 188, 144, 167, 157, 171, 175, 169, 180, 142, 125, 124, 191, 168, 186, 190, 203, 193, 183, 184, 176, 182, 168, 201, 147, 153, 162, 187, 162, 209, 141, 183],
# [236, 178, 137, 121, 138, 160, 181, 136, 132, 139, 150, 168, 124, 211, 169, 182, 245, 165, 216, 275, 171, 195, 220, 170, 174, 165, 140, 138, 148, 125, 171, 179, 204, 216, 252, 172, 177, 133, 191, 131, 179, 157, 137, 256, 185, 174, 143, 149, 123, 151, 137, 211, 255, 135, 169, 208, 137, 180, 140, 195, 192, 227, 236, 133, 183, 212, 163, 131, 162, 142, 151, 150, 180, 149, 145, 124, 160, 198, 152, 165, 198, 156, 136, 273, 147, 145, 144, 174, 187, 148, 169, 148, 172, 192, 174, 155, 149, 136, 181, 157],
# [231, 151, 135, 207, 225, 150, 151, 212, 140, 239, 171, 187, 248, 235, 145, 228, 196, 197, 287, 166, 156, 140, 159, 187, 201, 166, 175, 202, 140, 190, 133, 151, 148, 157, 184, 188, 144, 122, 196, 171, 257, 178, 113, 202, 138, 135, 161, 163, 206, 171, 163, 143, 177, 148, 170, 212, 141, 194, 146, 134, 175, 164, 196, 168, 138, 143, 186, 147, 264, 127, 136, 135, 176, 191, 148, 123, 177, 164, 131, 152, 162, 208, 169, 170, 230, 136, 151, 182, 154, 163, 292, 137, 189, 171, 213, 162, 247, 136, 151, 170],
# [171, 165, 164, 147, 225, 151, 133, 149, 144, 192, 158, 244, 160, 188, 173, 138, 122, 178, 201, 140, 208, 231, 149, 168, 154, 149, 148, 175, 197, 171, 234, 153, 152, 140, 355, 213, 148, 174, 142, 126, 155, 174, 202, 174, 143, 146, 145, 203, 160, 154, 231, 153, 165, 195, 178, 151, 191, 133, 192, 136, 188, 201, 172, 257, 146, 174, 193, 163, 153, 142, 174, 321, 140, 134, 244, 212, 147, 154, 176, 132, 160, 175, 234, 138, 163, 118, 193, 172, 195, 213, 214, 160, 113, 148, 129, 225, 220, 188, 182, 213],
# [136, 165, 177, 201, 138, 168, 156, 240, 243, 152, 136, 188, 134, 162, 183, 163, 166, 216, 135, 129, 164, 160, 129, 133, 144, 154, 205, 142, 198, 181, 157, 208, 138, 163, 196, 201, 177, 176, 221, 141, 115, 161, 139, 232, 175, 133, 158, 183, 210, 125, 150, 137, 152, 201, 161, 171, 142, 143, 204, 167, 159, 175, 150, 170, 110, 159, 172, 158, 148, 135, 202, 223, 170, 246, 186, 124, 143, 163, 211, 151, 187, 213, 186, 179, 173, 172, 153, 225, 148, 193, 272, 115, 180, 160, 176, 150, 148, 208, 157, 132],
# [177, 332, 147, 207, 132, 162, 133, 133, 195, 184, 174, 153, 158, 149, 171, 189, 108, 203, 131, 162, 225, 176, 143, 154, 279, 234, 181, 152, 223, 142, 139, 193, 138, 150, 168, 149, 155, 131, 136, 167, 126, 157, 209, 156, 153, 155, 136, 134, 147, 139, 149, 139, 159, 214, 130, 211, 143, 148, 137, 133, 156, 200, 201, 174, 161, 205, 131, 137, 157, 154, 170, 161, 237, 178, 180, 172, 124, 138, 143, 185, 126, 126, 151, 156, 159, 247, 177, 167, 168, 139, 142, 126, 150, 164, 156, 120, 172, 184, 148, 185],
# [159, 187, 206, 237, 147, 198, 224, 130, 167, 208, 240, 156, 216, 188, 141, 152, 131, 167, 146, 146, 178, 175, 139, 152, 183, 144, 248, 173, 132, 173, 180, 187, 148, 164, 159, 170, 139, 169, 231, 153, 156, 197, 204, 118, 190, 208, 137, 134, 188, 164, 132, 148, 124, 204, 235, 188, 144, 152, 188, 122, 177, 141, 143, 181, 168, 209, 191, 157, 152, 180, 184, 196, 132, 116, 123, 166, 184, 127, 164, 177, 148, 175, 135, 152, 141, 230, 159, 164, 188, 159, 143, 143, 141, 162, 183, 157, 147, 142, 148, 188],
# ]
# nn_traj_data = [
# [198, 206, 178, 158, 174, 172, 138, 136, 202, 416, 148, 168, 210, 560, 164, 184, 302, 202, 268, 210, 180, 298, 160, 238, 190, 356, 196, 184, 218, 140, 190, 254, 216, 226, 216, 190, 234, 188, 176, 146, 208, 168, 218, 276, 238, 184, 264, 162, 196, 190, 224, 170, 238, 134, 152, 174, 216, 350, 190, 246, 192, 174, 186, 682, 220, 204, 154, 144, 188, 176, 196, 162, 292, 190, 268, 228, 192, 216, 182, 232, 226, 194, 180, 224, 218, 166, 220, 184, 182, 158, 184, 310, 150, 130, 180, 184, 178, 188, 206, 194],
# [194, 194, 140, 276, 216, 142, 172, 204, 168, 164, 194, 232, 242, 132, 126, 194, 344, 142, 186, 148, 232, 152, 150, 248, 166, 170, 188, 196, 244, 160, 296, 168, 200, 178, 214, 236, 218, 140, 180, 166, 188, 224, 260, 130, 190, 122, 294, 208, 144, 168, 198, 238, 136, 218, 248, 234, 232, 156, 138, 158, 258, 188, 208, 164, 280, 250, 220, 142, 154, 182, 258, 236, 216, 176, 196, 202, 274, 268, 302, 400, 158, 212, 198, 152, 202, 202, 182, 142, 134, 248, 118, 168, 160, 314, 370, 204, 176, 178, 186, 150],
# ]
# nn_time_data = [
# [1.4462645419989713, 1.7088955150029506, 1.361946345998149, 1.3695014600016293, 1.5671857359993737, 1.481699056996149, 1.0677907689969288, 1.0918150110010174, 1.6448368850033148, 2.956772263001767, 1.130766610003775, 1.3165569060001872, 1.492839238999295, 3.1228196939991903, 1.3154978949969518, 1.6214799270019284, 2.2099633420002647, 1.5267259890024434, 2.034569035000459, 1.5807912589953048, 1.461429915005283, 2.1437796460013487, 1.3408771809990867, 1.958894108000095, 1.637375147001876, 2.5072193649975816, 1.4501445119967684, 1.7286867209986667, 1.610526900003606, 1.2609322850039462, 1.4866449640030623, 1.7460907530039549, 1.6500354439995135, 1.6812347960003535, 1.572385696999845, 1.3791274210016127, 1.7791926530044293, 1.4637708570007817, 1.3210894119984005, 1.238126561001991, 1.5753058370028157, 1.4708553770033177, 1.7796351209981367, 2.026341176999267, 1.4498691010012408, 1.5006212630032678, 2.0487539819951053, 1.2980627099968842, 1.5662024880002718, 1.5562543800042477, 1.7270862340010353, 1.5350803129986161, 1.7540661050006747, 1.1820562849970884, 1.2153055049930117, 1.5176550920004956, 1.8629052899996168, 2.285796620999463, 1.6908715270037646, 1.991654160003236, 1.3634275820004405, 1.5014306609955383, 1.632629353000084, 4.531791525994777, 1.863866973995755, 1.5935716189997038, 1.1897914500004845, 1.2630833810035256, 1.567113402998075, 1.427508641994791, 1.6500954360017204, 1.367469896998955, 2.382199089995993, 1.5909635170028196, 2.2305142379991594, 1.711697260994697, 1.6914257719981833, 1.5398958540026797, 1.4747449179994874, 1.7230304740005522, 1.7504821960028494, 1.5616723990024184, 1.3164274230002775, 1.6892336719974992, 1.6094090199985658, 1.3447162219963502, 1.5157135109984665, 1.5700635639950633, 1.5621553220044007, 1.3887742759980028, 1.3669046880022506, 2.3299648619940854, 1.2999092739992193, 1.0635495370006538, 1.3798666419970687, 1.5076081209990662, 1.3359034300010535, 1.5524928880040534, 1.558548189997964, 1.6895183870001347],
# [1.4504115989984712, 1.5137215349968756, 1.1622854930028552, 2.041229182999814, 1.711910677004198, 1.1075217110046651, 1.3842580400014413, 1.5851321219961392, 1.4574365169974044, 1.375445090001449, 1.6561897060018964, 1.8218259740024223, 1.8567485329986084, 1.133061666994763, 1.0824113999988185, 1.4250733490043785, 2.421184942999389, 1.261483969996334, 1.3674899180041393, 1.1437649149956997, 1.7144728459970793, 1.2725698549984372, 1.260526967002079, 1.8355907760051196, 1.2751189100017655, 1.3319198090030113, 1.4852830349991564, 1.5170181389985373, 2.0113240930004395, 1.3794324949994916, 2.297262980006053, 1.4246883660016465, 1.6256173689980642, 1.3537732810000307, 1.6936436590040103, 1.7586271629988914, 1.7012998569989577, 1.093642608000664, 1.4618213540015859, 1.1387302250004723, 1.3610037839971483, 1.5712911130030989, 1.9385262889991282, 1.0183718569969642, 1.4017351840011543, 1.0468262499998673, 1.97225828399678, 1.6544202570003108, 1.2952454659971409, 1.3765625430023647, 1.4951180589996511, 2.111654845000885, 1.3042532300023595, 1.7988734469981864, 1.8699254210005165, 2.0605206780019216, 1.6530067200001213, 1.32013470500533, 1.191049490000296, 1.3372293620050186, 1.8475971349980682, 2.3003795240001637, 2.4239870899982634, 2.3174276360005024, 1.9914600330012036, 1.9868471890004002, 1.6869703020056477, 1.2174907840017113, 1.2701826710035675, 1.5897011569977622, 1.837059165998653, 1.7019206950062653, 1.703163393001887, 1.6077323589997832, 1.4664895879977848, 1.5578441969992127, 2.254363836997072, 2.014105900998402, 2.288929766000365, 2.720100979000563, 1.367125153003144, 1.557388383000216, 1.3633819360038615, 1.4611007810017327, 1.7401004769999417, 1.648301409004489, 1.2716110490000574, 1.2832517150018248, 1.1009501360022114, 1.9707238739938475, 1.089886841000407, 1.4893638449939317, 1.3898896640021121, 2.3699478070047917, 2.840002678000019, 1.6281013840052765, 1.633849243997247, 1.4584663139976328, 1.2724822099989979, 1.3325593629997456],
# ]
# cnn_path_data =[
# [178, 182, 139, 146, 185, 129, 129, 130, 218, 292, 159, 205, 174, 295, 142, 156, 167, 152, 192, 196, 132, 213, 155, 190, 164, 188, 154, 199, 134, 117, 165, 187, 185, 136, 165, 158, 208, 149, 150, 140, 164, 147, 172, 198, 183, 131, 177, 147, 172, 159, 163, 136, 177, 126, 145, 149, 183, 214, 164, 205, 123, 157, 159, 214, 168, 168, 150, 134, 191, 152, 145, 145, 164, 216, 169, 245, 195, 208, 188, 365, 205, 217, 181, 186, 138, 151, 144, 149, 171, 146, 188, 175, 259, 122, 145, 204, 172, 183, 147, 274],
# [169, 154, 181, 175, 162, 130, 173, 185, 161, 131, 175, 192, 134, 126, 139, 159, 164, 194, 162, 133, 177, 144, 138, 179, 149, 167, 173, 155, 160, 158, 247, 134, 166, 126, 171, 179, 156, 130, 134, 133, 180, 179, 165, 129, 130, 114, 196, 150, 163, 160, 146, 185, 128, 166, 194, 204, 187, 141, 130, 155, 185, 142, 168, 192, 158, 141, 143, 165, 135, 170, 270, 197, 178, 177, 149, 162, 199, 231, 147, 219, 150, 148, 157, 142, 150, 188, 162, 142, 142, 210, 115, 157, 153, 299, 173, 199, 144, 168, 143, 142],
# ]
# cnn_traj_data = [
# [198, 210, 152, 164, 210, 136, 140, 146, 268, 338, 170, 282, 192, 416, 158, 186, 208, 186, 212, 234, 150, 256, 178, 228, 180, 240, 180, 256, 150, 126, 192, 252, 216, 152, 200, 178, 246, 162, 162, 146, 186, 162, 206, 256, 216, 140, 238, 162, 190, 192, 180, 158, 224, 134, 168, 160, 202, 298, 184, 224, 136, 174, 186, 288, 190, 188, 154, 142, 234, 184, 164, 160, 190, 324, 198, 284, 256, 260, 208, 466, 248, 252, 230, 222, 152, 166, 160, 166, 190, 162, 216, 218, 338, 130, 158, 256, 212, 214, 152, 364],
# [206, 176, 196, 198, 180, 138, 194, 210, 178, 146, 214, 214, 142, 132, 150, 188, 178, 236, 184, 140, 210, 152, 146, 214, 158, 198, 190, 182, 210, 178, 274, 148, 182, 134, 190, 222, 170, 138, 140, 146, 196, 214, 196, 138, 142, 120, 260, 172, 176, 186, 174, 214, 142, 186, 236, 258, 210, 164, 138, 180, 266, 166, 216, 246, 178, 154, 156, 192, 140, 182, 330, 226, 214, 210, 166, 188, 244, 292, 168, 292, 158, 160, 186, 150, 182, 204, 174, 152, 154, 232, 118, 182, 180, 396, 218, 244, 158, 202, 172, 154],
# ]
# cnn_time_data = [
# [3.1681611710009747, 3.4488704670002335, 2.5972534750035265, 2.39050128999952, 3.2114720739991753, 2.21953018700151, 2.2693315770011395, 2.1936211850043037, 4.004001472996606, 5.266598957001406, 2.719514608994359, 3.7566687229991658, 2.8973439629990025, 5.8524786829948425, 2.54256507899845, 3.073332918997039, 2.9624054540036013, 2.679070022000815, 3.3946765509972465, 3.393356473003223, 2.131488144004834, 3.7603600369984633, 2.657604979998723, 3.5237622869972256, 2.9966810870027984, 3.287517933000345, 2.8187359319999814, 3.647578831994906, 2.334226947998104, 2.425244407997525, 2.562130715996318, 3.546778969001025, 3.306786970999383, 2.3190064980008174, 2.7759447139978874, 2.748246379996999, 3.6393377860003966, 2.2729815380007494, 2.6706092660024296, 2.4872900230038795, 2.9862147189996904, 2.463295052999456, 3.235506531003921, 3.5093516210035887, 3.307985501996882, 2.0174351430032402, 2.876303734999965, 2.428748362995975, 3.375652690003335, 3.0004235390006215, 2.686729392000416, 2.7738840309975785, 3.5097064369983855, 2.193867797999701, 2.1587642910017166, 2.7350801730062813, 3.175484421000874, 3.7803448620034033, 2.961349263001466, 3.4810102789997472, 1.93788139600656, 3.102783415997692, 3.281143915002758, 4.547659856994869, 3.6292848410012084, 3.3342111149977427, 2.7366149719964596, 2.5005846380008734, 3.641561188000196, 2.79611100400507, 2.5080093610013137, 2.587433703993156, 3.0924017799989088, 4.064349804000813, 3.0644004969944945, 4.892293147000601, 4.038530581994564, 4.295947452999826, 3.74842912999884, 6.897448293995694, 3.745304677002423, 3.878832918999251, 3.138111257998389, 3.409589037000842, 2.3090972069985582, 2.6011260119994404, 2.420690259998082, 2.8477310970047256, 2.9397604209952988, 2.478690045994881, 3.305076871998608, 3.2803818749962375, 4.56525243500073, 2.1952713069986203, 2.466344464002759, 3.717819346995384, 2.593732893001288, 3.0792648589995224, 2.2692328090051888, 4.546211059001507],
# [2.9352778350003064, 2.6295722520007985, 3.304458691003674, 3.1032361170000513, 2.7665672970033484, 2.111409345998254, 3.2600351959990803, 3.3958402250063955, 3.2778234599973075, 2.549487885000417, 3.539209075999679, 3.3255862110017915, 2.154346262002946, 2.0528118280053604, 2.5815510890024598, 3.2778984589967877, 2.6950241529993946, 3.757954674998473, 2.6924402369986637, 2.1856680310011143, 3.2522206470021047, 2.450288427993655, 2.393204070001957, 3.5483487370001967, 2.582777345996874, 2.8061551520004286, 3.0597442100042826, 2.7193629489993327, 2.8052823940015514, 2.733398912998382, 4.248718199996802, 2.4132908469982794, 2.932154548994731, 2.230320798997127, 3.108970108005451, 3.3919590239966055, 2.628881643002387, 2.1138404999946943, 2.108195180000621, 2.3336283349999576, 3.257250654998643, 3.336392330005765, 3.467787073001091, 2.1202006910025375, 1.9102215129969409, 1.9083302529979846, 3.6138152300045476, 2.789393573999405, 2.7651154149934882, 2.514866298995912, 2.7682248769997386, 3.5333717159955995, 2.360732624998491, 3.465297296999779, 3.942351227000472, 3.847256548004225, 3.1378387049990124, 2.409698789997492, 2.125815118997707, 2.5057235290005337, 4.536037727004441, 4.019048437003221, 5.548169278001296, 5.136147662997246, 3.0572892850032076, 2.479010286995617, 2.4478035350039136, 3.142310513998382, 2.4767421310025384, 2.9505696230044123, 4.980051755002933, 3.187295167001139, 3.057738083000004, 2.9806741420034086, 2.854782365000574, 3.5710944370002835, 3.596790516996407, 4.576518321999174, 2.740291005000472, 4.123230744000466, 2.472417308999866, 2.5566429489990696, 2.7170862979983212, 2.6116838620000635, 2.5988648330021533, 3.3835079269993003, 2.5776510770010646, 2.963742710002407, 2.7303960749995895, 3.8479974700021558, 1.9144705860016984, 2.8661852720033494, 2.8295660099975066, 5.925010934006423, 3.803017469996121, 3.5116002290014876, 2.236997373998747, 2.729357700001856, 2.672384099998453, 2.2958169620033004],
# ]
# a_path_data =[
# [172, 201, 134, 137, 153, 129, 129, 127, 174, 283, 159, 144, 174, 231, 142, 240, 164, 150, 184, 184, 153, 206, 144, 189, 161, 188, 147, 186, 134, 116, 162, 185, 175, 162, 161, 160, 207, 149, 147, 140, 162, 144, 172, 192, 229, 131, 177, 147, 150, 163, 162, 153, 176, 126, 144, 149, 183, 211, 164, 216, 129, 157, 196, 185, 166, 168, 150, 143, 177, 152, 144, 143, 179, 181, 155, 177, 177, 148, 162, 188, 150, 206, 157, 186, 193, 151, 144, 149, 174, 146, 166, 157, 221, 120, 145, 190, 152, 179, 154, 164],
# [165, 155, 157, 191, 175, 129, 179, 163, 158, 129, 154, 186, 134, 123, 139, 148, 164, 192, 160, 133, 168, 144, 137, 198, 149, 172, 155, 155, 159, 158, 256, 136, 166, 124, 158, 176, 156, 130, 134, 136, 180, 176, 168, 128, 132, 114, 181, 171, 138, 155, 153, 165, 128, 164, 160, 191, 185, 137, 133, 155, 182, 146, 165, 181, 158, 140, 143, 159, 135, 168, 265, 194, 178, 150, 147, 162, 185, 207, 147, 253, 148, 148, 158, 142, 163, 166, 162, 140, 142, 204, 115, 199, 150, 285, 162, 183, 141, 168, 134, 142],
# ]
# a_traj_data = [
# [246, 315, 202, 204, 233, 185, 189, 185, 266, 426, 221, 213, 268, 427, 214, 384, 262, 245, 267, 281, 239, 300, 211, 302, 249, 315, 226, 310, 206, 178, 235, 317, 275, 269, 263, 262, 332, 206, 216, 208, 242, 203, 282, 319, 414, 182, 307, 210, 237, 248, 240, 231, 294, 180, 202, 221, 275, 361, 252, 327, 187, 242, 313, 315, 266, 253, 207, 208, 283, 232, 212, 209, 286, 316, 242, 325, 317, 246, 261, 304, 238, 337, 234, 298, 299, 233, 211, 237, 275, 225, 273, 244, 366, 168, 205, 324, 221, 287, 240, 237],
# [261, 243, 241, 302, 266, 178, 276, 246, 245, 205, 230, 289, 190, 164, 203, 225, 251, 303, 231, 181, 260, 209, 200, 307, 215, 265, 233, 239, 260, 241, 363, 209, 256, 189, 248, 295, 222, 182, 185, 211, 273, 280, 274, 175, 186, 163, 309, 305, 198, 243, 256, 243, 200, 240, 274, 298, 279, 211, 201, 235, 326, 226, 292, 299, 254, 211, 215, 250, 186, 256, 449, 291, 287, 224, 208, 262, 312, 350, 217, 446, 213, 204, 260, 206, 249, 265, 223, 200, 197, 312, 156, 302, 239, 476, 263, 282, 201, 251, 186, 201],
# ]
# a_time_data = [
# [0.03764109400071902, 0.0665087639936246, 0.055336618002911564, 0.040562173002399504, 0.04211132999625988, 0.03701496499706991, 0.029527172999223694, 0.03046992699819384, 0.052500756006338634, 0.11247568399994634, 0.03238277600030415, 0.04376925800170284, 0.036870178002573084, 0.09187774400197668, 0.03047523599525448, 0.08022449100099038, 0.0431034740031464, 0.04383408099965891, 0.0401312269968912, 0.03998642199439928, 0.039533506002044305, 0.057588183000916615, 0.03489538400026504, 0.05877347600471694, 0.04503441700217081, 0.06283477300166851, 0.03367466400231933, 0.05791689600300742, 0.03354507999756606, 0.04060867600492202, 0.03304345200012904, 0.07326998899952741, 0.05628500199964037, 0.05682707199594006, 0.053689710002799984, 0.05700245999469189, 0.05808545000036247, 0.040780000999802724, 0.04950576599367196, 0.05084559500392061, 0.0429189580027014, 0.048003872005210724, 0.041323500001453795, 0.04930623900145292, 0.08477202700305497, 0.031486784006119706, 0.11072091100504622, 0.034518994005338755, 0.05179514999326784, 0.06562065599428024, 0.03846069800056284, 0.07542236299923388, 0.04859672699967632, 0.03368868500547251, 0.027400225997553207, 0.04908360500121489, 0.04184313500445569, 0.07303698199393693, 0.035634817999380175, 0.10317792299611028, 0.03038611500232946, 0.034473084000637755, 0.05132676699577132, 0.06860959399637068, 0.10102555200137431, 0.03964684499806026, 0.033419158004107885, 0.07612029400479514, 0.05185671000072034, 0.06676756500382908, 0.030711729006725363, 0.03168183499656152, 0.03670132899424061, 0.07147242700011702, 0.041541025006154086, 0.07393235699419165, 0.06053043800056912, 0.037619652997818775, 0.05440364500100259, 0.06293175499740755, 0.05568182199931471, 0.06096842400438618, 0.03585370800283272, 0.048104785004397854, 0.07540274199709529, 0.0364063380038715, 0.039604245001100935, 0.03865141999995103, 0.06167639000341296, 0.04609003199584549, 0.038729963001969736, 0.05075645900069503, 0.07289155200123787, 0.03347561300324742, 0.02754861499852268, 0.047877056000288576, 0.0515510799959884, 0.08050083499983884, 0.04470267800206784, 0.04011809099756647],
# [0.06059766700491309, 0.044321524001134094, 0.044852432001789566, 0.05600145799689926, 0.04371619399898918, 0.034987535000254866, 0.04593473400018411, 0.035924358999182004, 0.05700145500304643, 0.048054200000478886, 0.036137909002718516, 0.052905071002896875, 0.036891600997478236, 0.024210422998294234, 0.030869342997903004, 0.03456927899969742, 0.05486399600340519, 0.049145509001391474, 0.0292262840011972, 0.031160965001618024, 0.04314926200459013, 0.03736786099761957, 0.03871891900053015, 0.058568262997141574, 0.034802846996171866, 0.06539985199924558, 0.03965758399863262, 0.06704498599719955, 0.05524629499996081, 0.040190864994656295, 0.07270387899916386, 0.0407109540028614, 0.04606305899505969, 0.03648358999635093, 0.04739266200340353, 0.056367481003690045, 0.0343653070012806, 0.028153966006357223, 0.031423766995430924, 0.040090016002068296, 0.04929436400561826, 0.04215431299962802, 0.0690132480012835, 0.03548841000156244, 0.034863436994783115, 0.029178277996834368, 0.09049790399876656, 0.07526266600325471, 0.031259764000424184, 0.03934472100081621, 0.06213217500044266, 0.04164046500227414, 0.04282726800011005, 0.03346603699901607, 0.0558953189975, 0.07174053600465413, 0.04245580400311155, 0.050032202001602855, 0.03799430999788456, 0.044824462005635723, 0.11715058999834582, 0.06697170199913671, 0.14550606600096216, 0.04877648500405485, 0.0633019150045584, 0.03407352700014599, 0.03742479899665341, 0.038927824003621936, 0.042967663997842465, 0.0609712329969625, 0.09092714000144042, 0.04700433099787915, 0.05556646500190254, 0.0371584710010211, 0.02929646100528771, 0.04669775700313039, 0.0539834939991124, 0.05742871400434524, 0.03565712099953089, 0.07697771699895384, 0.029064542999549303, 0.02602607200242346, 0.03956666799786035, 0.040727680003328715, 0.038130745000671595, 0.04621806100476533, 0.02929288100131089, 0.06483432200184325, 0.028797492996091023, 0.05589781500020763, 0.02958510299504269, 0.058332075001089834, 0.11833356100396486, 0.16752738200011663, 0.05765397800132632, 0.05956849700305611, 0.03988447099982295, 0.05109297700255411, 0.028284004001761787, 0.030339450000610668],
# ]
# # Remaining data not yet pput into arrays (groups of 100)
# NN Traj [158, 286, 188, 174, 242, 140, 156, 152, 180, 142, 192, 196, 258, 178, 158, 176, 266, 288, 308, 182, 226, 210, 144, 232, 230, 156, 236, 178, 224, 188, 214, 216, 232, 152, 228, 194, 144, 302, 304, 190, 250, 340, 196, 156, 184, 360, 152, 248, 172, 350, 172, 194, 204, 144, 160, 170, 288, 168, 208, 218, 184, 154, 226, 206, 210, 232, 280, 160, 158, 232, 166, 204, 186, 218, 246, 234, 206, 164, 132, 140, 258, 218, 220, 256, 232, 288, 216, 274, 208, 242, 190, 236, 174, 180, 172, 228, 198, 254, 164, 268]
# NN Time [1.3212577890008106, 1.9925428720016498, 1.3859341220013448, 1.4921062669964158, 1.965525075996993, 1.0918242190018645, 1.383629554999061, 1.2273182930002804, 1.6578422710008454, 1.0917626789960195, 1.6642442839947762, 1.5164902140022605, 1.8275394200027222, 1.401741535999463, 1.289632284999243, 1.3692463170009432, 2.0517160320014227, 2.1291743889960344, 2.3463153379998403, 1.2555686500054435, 1.8674906399974134, 1.4713658559994656, 1.2121894880037871, 1.6551104230020428, 1.7551386889972491, 1.238382997995359, 1.9709356849998585, 1.3084131499999785, 1.6703547579963924, 1.5418989840036375, 1.5615753020028933, 1.7052438819955569, 1.6515617219993146, 1.2367673230037326, 1.5349027129996102, 1.4816381900018314, 1.1258776110043982, 2.4193257439983427, 2.3871968629973708, 2.6267324489963357, 2.7794182319994434, 4.116087060996506, 3.523471344000427, 1.7980555339963757, 1.4649449959979393, 2.96255233699776, 1.3810303479986032, 2.073908033002226, 1.392418313997041, 2.508210179003072, 1.7352602049941197, 1.5681698729968048, 1.9273307109979214, 1.1843873539983178, 1.56123356900207, 1.8738530909977271, 2.1801255000027595, 1.3677090589990257, 1.8160756040015258, 1.751122685993323, 1.1401434490035172, 1.197604050001246, 1.7129510140002822, 1.5621954550006194, 1.5126225510030054, 1.6719115700034308, 1.8650925140027539, 1.3389352570011397, 1.1960232900019037, 1.8027359010011423, 1.2123910179943778, 1.6604682120014331, 1.36814405000041, 1.5568391190026887, 1.7133855099964421, 1.4436364029970719, 1.6422913339993102, 1.2569241259989212, 1.0541661610041047, 1.0454005009960383, 1.5958859980019042, 1.6708123909993446, 1.6902598020024016, 1.7733599159982987, 1.833881590006058, 2.0393629140016856, 1.719179918000009, 2.0140843690023758, 1.7394078900033492, 1.8313406050001504, 1.7330921989996568, 1.8716963350016158, 1.3781554410015815, 1.48177477200079, 1.36401301099977, 1.9833854219978093, 1.428101715995581, 1.6270873530011158, 1.2846593180001946, 1.8905200650042389]
# CNN Path [155, 186, 156, 153, 189, 128, 134, 122, 167, 135, 155, 186, 193, 150, 133, 141, 162, 161, 234, 156, 173, 127, 115, 147, 161, 137, 193, 158, 152, 168, 184, 165, 173, 131, 135, 162, 128, 145, 215, 156, 151, 153, 161, 154, 177, 218, 154, 187, 155, 265, 147, 142, 163, 123, 145, 141, 198, 184, 185, 180, 149, 138, 185, 165, 130, 128, 204, 154, 150, 198, 135, 163, 147, 159, 147, 190, 165, 152, 133, 123, 187, 156, 212, 191, 174, 200, 191, 195, 226, 161, 168, 186, 136, 232, 152, 174, 157, 209, 146, 151]
# CNN Traj [182, 238, 196, 170, 246, 144, 138, 128, 194, 142, 170, 228, 218, 168, 152, 156, 180, 194, 312, 174, 202, 142, 122, 154, 208, 154, 220, 176, 174, 186, 232, 190, 210, 144, 150, 184, 132, 160, 258, 174, 178, 172, 188, 156, 196, 258, 178, 244, 172, 358, 158, 164, 216, 130, 158, 156, 228, 224, 214, 200, 170, 150, 214, 184, 144, 138, 250, 202, 178, 226, 148, 176, 164, 178, 170, 252, 186, 178, 138, 130, 238, 176, 284, 246, 184, 246, 232, 266, 286, 184, 196, 216, 142, 300, 194, 198, 176, 248, 176, 162]
# CNN Time [2.711082063993672, 3.360159816002124, 2.4103589779988397, 2.814428728997882, 3.9095874690028722, 2.343037851002009, 2.5263848089962266, 1.9235784070042428, 2.7977815010017366, 2.1468019550011377, 2.9434080020000692, 3.6278398399954312, 3.4232137280050665, 2.75045240700274, 2.039091312995879, 2.5355182910061558, 3.0564265240027453, 3.133683671003382, 4.309712808004406, 2.823043731004873, 3.2579220879997592, 2.2821742500018445, 2.0027810159954242, 2.3793237799982307, 2.9308623199976864, 2.175165319000371, 3.5040336800011573, 2.508222181000747, 2.4399698410052224, 2.951628137998341, 3.430839189000835, 2.9938548259960953, 3.3077243320003618, 2.3336603689967887, 2.30668538199825, 2.82677543599857, 1.9464514600040275, 2.4404622389993165, 7.249775797004986, 5.745624082999711, 5.3269884979963535, 8.641017690999433, 7.737131839996437, 2.895719125997857, 3.799083691003034, 4.672995979999541, 2.816895960997499, 3.9647072269945056, 2.9998880150014884, 6.232357878005132, 3.347415946998808, 3.205842149000091, 2.9803414500056533, 1.9407362510028179, 2.9318480500005535, 2.7663469029939733, 3.44031678999454, 3.7135396090016, 3.7496643609993043, 3.2510515229951125, 2.5566094040041207, 2.381697887998598, 3.298104030000104, 2.65121609599737, 2.169672404997982, 1.950294741000107, 3.5918463979978696, 2.581729618999816, 2.8209513889960363, 3.49545944500278, 2.094565125000372, 2.8403869599933387, 2.3321344229989336, 2.8226805529993726, 2.587849434996315, 3.3409201279937406, 2.9403476399966166, 2.4944126210029935, 2.092010244996345, 1.954739177999727, 3.301532457000576, 2.7501605249999557, 3.8731951910012867, 3.2537436069978867, 3.0512338699991233, 3.6181718919979176, 3.409836382998037, 3.5232774070027517, 4.1151843030020245, 2.8276591839967296, 2.934140084995306, 3.068190054000297, 2.4336851369953365, 4.413591144999373, 2.8378188950009644, 3.071988056995906, 2.6329255890013883, 3.6279318000015337, 2.5155894560011802, 2.8506642369975452]
# A Path [148, 185, 157, 159, 155, 128, 134, 122, 170, 135, 163, 177, 190, 153, 133, 141, 162, 159, 228, 155, 162, 138, 115, 147, 161, 137, 194, 156, 210, 160, 161, 166, 167, 139, 135, 161, 128, 145, 210, 156, 151, 152, 164, 154, 164, 212, 186, 168, 167, 241, 147, 139, 157, 144, 151, 161, 197, 179, 161, 180, 145, 138, 183, 175, 130, 128, 201, 151, 139, 185, 132, 163, 134, 145, 147, 205, 165, 149, 126, 123, 158, 167, 227, 183, 173, 200, 175, 186, 222, 156, 165, 127, 136, 144, 167, 167, 156, 208, 141, 151]
# A Traj [219, 323, 256, 248, 237, 190, 192, 158, 260, 177, 261, 284, 286, 239, 202, 222, 255, 273, 383, 223, 272, 230, 171, 204, 273, 200, 319, 228, 365, 249, 228, 263, 263, 227, 201, 243, 165, 209, 350, 232, 236, 240, 262, 200, 253, 338, 283, 265, 250, 410, 217, 211, 247, 223, 226, 250, 306, 274, 264, 269, 209, 200, 279, 262, 192, 172, 324, 239, 215, 291, 187, 250, 210, 216, 227, 352, 252, 221, 174, 170, 239, 283, 393, 297, 247, 332, 290, 344, 401, 251, 271, 185, 192, 232, 256, 254, 231, 315, 217, 237]
# A Time [0.04326045200286899, 0.051850573006959166, 0.05430974200135097, 0.05303594500583131, 0.04570116399554536, 0.039044782002747525, 0.03518680100387428, 0.02227607099484885, 0.06342742999549955, 0.035134876998199616, 0.04856938600278227, 0.06087633300194284, 0.04494999199960148, 0.042730002001917455, 0.04018744900531601, 0.040613445002236404, 0.04560205199959455, 0.06288264899922069, 0.08604311799717834, 0.031193105998681858, 0.061960535997059196, 0.05067266499827383, 0.034087302003172226, 0.039914455999678466, 0.06953652700030943, 0.038544707000255585, 0.062337283001397736, 0.031694805998995434, 0.07207159799872898, 0.04633512000145856, 0.036190220998832956, 0.05062284800078487, 0.04506821499671787, 0.03770229600195307, 0.03713996899750782, 0.040979500001412816, 0.017390916000294965, 0.03442163900035666, 0.08519965400046203, 0.06763657999545103, 0.11181949300225824, 0.1360999480020837, 0.08034329499787418, 0.031582003000949044, 0.05417806899640709, 0.10522602999844821, 0.06454800099891145, 0.042256225999153685, 0.046528282000508625, 0.0780488050004351, 0.05487411700596567, 0.05783274599525612, 0.05470834100560751, 0.03906335899955593, 0.057480971998302266, 0.03930255400337046, 0.04133783899305854, 0.04202608700143173, 0.0506966890025069, 0.05153069700463675, 0.03172251100477297, 0.039150333002908155, 0.039017419003357645, 0.061327715004154015, 0.030289039001218043, 0.024384370997722726, 0.056557789001089986, 0.05720518199814251, 0.0410596829970018, 0.07504594799684128, 0.026770820004458074, 0.04000819499924546, 0.04284277799888514, 0.041523981002683286, 0.03336424199369503, 0.06809560300462181, 0.04300578399852384, 0.0521691899994039, 0.030581840001104865, 0.027566729004320223, 0.039313431007030886, 0.046262224997917656, 0.08027823999873362, 0.05241790999571094, 0.04242302400234621, 0.053181338000285905, 0.06403886499902, 0.06517877699661767, 0.07930802500050049, 0.03836063499329612, 0.05043060699972557, 0.029304352996405214, 0.028831475996412337, 0.04270634899876313, 0.04144349200214492, 0.04035905199998524, 0.03629380899656098, 0.053951046997099183, 0.030079632000706624, 0.03998750800383277]
# starting iteration 3
# NN Traj [316, 242, 144, 130, 148, 198, 204, 148, 148, 156, 180, 184, 130, 308, 194, 242, 374, 188, 304, 382, 212, 244, 290, 212, 192, 206, 156, 154, 166, 132, 194, 240, 264, 268, 366, 200, 214, 150, 252, 144, 204, 184, 146, 368, 254, 204, 152, 178, 126, 172, 148, 254, 328, 148, 194, 278, 150, 224, 160, 258, 248, 304, 324, 154, 242, 262, 192, 152, 198, 168, 170, 178, 254, 190, 162, 140, 196, 252, 168, 192, 258, 180, 164, 370, 178, 168, 150, 236, 210, 168, 182, 168, 238, 268, 206, 174, 190, 154, 220, 184]
# NN Time [2.3131831509963376, 1.9592696899999282, 1.1757495279962313, 1.1162934629974188, 1.2056505209984607, 1.628381795999303, 1.6193115950009087, 1.184142060999875, 1.1214932040020358, 1.1213788320019376, 1.3345970379959908, 1.5084329419987625, 1.0881288469972787, 2.5816097439965233, 1.466197301997454, 1.9190272750056465, 2.5244659680029145, 1.577415909996489, 2.190823803000967, 3.0233920050013694, 1.7538702970050508, 1.8991334689999348, 2.1044306859985227, 1.4652353779965779, 1.6574595999991288, 1.6027719570047338, 1.0961469290050445, 1.2187497180057107, 1.3944048289995408, 1.1185819960010122, 1.540080181999656, 1.7789756970014423, 1.9221053319997736, 1.9515726099998574, 2.581804976995045, 1.6516621420014417, 1.6605321960014408, 1.1264536750022671, 1.853592708997894, 1.2055239629989956, 1.463603886004421, 1.525715436997416, 1.3319383150010253, 2.6544540179966134, 2.0457256009976845, 1.5161012000025949, 1.4016927749980823, 1.4130931040053838, 1.005016158997023, 1.3371255149977515, 1.1590161040003295, 1.91730419400119, 2.4123229239994544, 1.0638457710010698, 1.5192504820006434, 2.149744362999627, 1.270587869999872, 1.9520225710002705, 1.1762785620012437, 1.9623558989987941, 1.745537955997861, 2.3166746979986783, 2.115075314002752, 1.203904574002081, 1.89231960199686, 1.7100835259989253, 1.543843484003446, 1.1807803330011666, 1.5204758899999433, 1.3458891150003183, 1.2529576779998024, 1.359813001996372, 1.7120116930018412, 1.4216985430030036, 1.3822036589990603, 1.2067782660014927, 1.4894425560050877, 1.7831931350010564, 1.3058568600026774, 1.3054844119978952, 2.059465950995218, 1.4581472459976794, 1.270023143995786, 2.62894125800085, 1.3782471529993927, 1.3241366399961407, 1.2472128329973202, 1.5665804310046951, 1.6826258090004558, 1.189605464001943, 1.4288445909987786, 1.5222388010006398, 1.7531571130020893, 1.815441214996099, 1.4357464810018428, 1.3139854370019748, 1.5090739529987331, 1.1563964579981985, 1.6351650880023954, 1.614636698999675]
# CNN Path [260, 192, 135, 121, 147, 180, 170, 136, 142, 139, 138, 167, 124, 213, 228, 232, 202, 233, 176, 267, 188, 194, 320, 176, 158, 142, 142, 143, 149, 146, 169, 129, 194, 171, 252, 138, 172, 130, 162, 130, 190, 149, 137, 251, 259, 150, 158, 142, 120, 152, 138, 200, 214, 134, 148, 220, 137, 183, 153, 197, 152, 176, 182, 139, 132, 193, 150, 137, 147, 136, 150, 133, 197, 272, 265, 123, 164, 175, 126, 165, 219, 155, 131, 262, 137, 141, 142, 219, 174, 148, 164, 139, 170, 150, 161, 158, 133, 131, 177, 161]
# CNN Traj [342, 228, 154, 130, 154, 216, 192, 146, 156, 150, 154, 184, 130, 270, 288, 282, 302, 308, 206, 330, 228, 244, 516, 206, 176, 156, 158, 160, 166, 156, 186, 142, 224, 190, 344, 152, 202, 136, 200, 142, 234, 182, 146, 354, 364, 176, 174, 160, 122, 170, 150, 232, 256, 144, 168, 258, 152, 216, 172, 230, 166, 218, 202, 164, 148, 228, 160, 158, 174, 154, 160, 140, 244, 362, 354, 132, 186, 218, 132, 180, 296, 174, 138, 334, 172, 160, 146, 308, 182, 168, 174, 154, 206, 176, 182, 176, 146, 146, 202, 178]
# CNN Time [4.945114360998559, 3.3420685659948504, 2.351106369002082, 2.0140302859945223, 2.2618696770005045, 3.3318038859943044, 2.9137950820004335, 2.2930109279986937, 2.416666309996799, 2.1032558379956754, 2.400186106999172, 3.0540676079981495, 1.9901796339981956, 4.049380112999643, 4.105204333005531, 4.355358982000325, 3.895965024996258, 4.640226133997203, 2.9836677450002753, 5.148707915002888, 3.4758050960008404, 3.5822699530035607, 5.769960819998232, 3.045340117998421, 2.765636860000086, 2.522148705997097, 2.52376502900006, 2.425705886002106, 2.7079717130036443, 2.4153163349983515, 2.866817722999258, 2.2354098970026826, 3.408465298001829, 2.8238023110025097, 4.924985402998573, 2.415222811003332, 2.899627496000903, 2.254958543002431, 2.996228123003675, 2.2130638179951347, 3.364184245998331, 2.618534016000922, 2.434872620004171, 5.046626000999822, 5.355156862999138, 2.6763259959989227, 2.737001096997119, 2.3698084880015813, 1.7947202659997856, 2.6278330109998933, 2.2366199679963756, 3.7698755279998295, 4.018575535999844, 2.0024352010004804, 2.3574645780026913, 4.100198995998653, 2.2973408230027417, 3.713311583000177, 2.720891043994925, 3.9146145330014406, 2.378712023004482, 3.0677492530012387, 2.9915382440012763, 2.2120918209984666, 2.3063199410025845, 3.295006487998762, 2.5729798860047595, 2.084448137997242, 2.584196632000385, 2.4350837120000506, 2.4216039719976834, 2.2817018979985733, 3.85980581700278, 4.994538202001422, 4.647876504001033, 2.0678355990021373, 2.8701243640025496, 3.372562086005928, 2.1334525019992725, 2.4640426599944476, 4.0792334619982284, 3.0081565590007813, 2.0114090879942523, 4.944621315000404, 2.532898714998737, 2.4456944779958576, 2.198961814996437, 3.749500978003198, 2.7139484970030026, 2.5658725239991327, 2.8190564640026423, 2.2420506100024795, 3.3405181810012436, 2.5494875040021725, 2.5462262720029685, 2.7616324390037335, 2.3030392820000998, 2.2553941719961585, 2.986115419997077, 2.7509792650016607]
# A Path [244, 189, 132, 121, 139, 163, 169, 136, 142, 139, 138, 177, 124, 204, 193, 230, 199, 217, 179, 223, 186, 178, 193, 166, 159, 142, 137, 137, 149, 146, 169, 129, 194, 171, 226, 137, 156, 132, 175, 130, 193, 143, 137, 221, 157, 138, 157, 188, 120, 152, 136, 199, 203, 134, 172, 198, 134, 176, 141, 196, 152, 173, 190, 134, 147, 190, 165, 126, 145, 136, 148, 144, 188, 204, 250, 123, 141, 173, 126, 165, 184, 155, 127, 231, 148, 140, 142, 154, 176, 148, 165, 167, 164, 146, 160, 159, 153, 131, 169, 162]
# A Traj [428, 304, 201, 177, 197, 281, 253, 196, 216, 195, 206, 272, 176, 332, 307, 373, 367, 363, 271, 389, 304, 294, 333, 266, 245, 222, 199, 208, 225, 214, 246, 197, 295, 248, 401, 199, 240, 185, 299, 186, 307, 218, 205, 388, 261, 195, 227, 335, 147, 231, 196, 316, 324, 191, 251, 314, 202, 289, 202, 306, 217, 289, 299, 195, 231, 296, 248, 193, 234, 215, 208, 215, 308, 336, 420, 185, 216, 287, 172, 228, 308, 238, 176, 378, 259, 212, 195, 232, 245, 227, 242, 261, 276, 225, 237, 249, 235, 203, 265, 249]
# A Time [0.08892450200073654, 0.04267270600394113, 0.03693932099849917, 0.03570451599807711, 0.028758701002516318, 0.055100133999076206, 0.0348225839989027, 0.030272582996985875, 0.03910133199678967, 0.03030911899986677, 0.0352981020041625, 0.040246546996058896, 0.031535709000309, 0.05188021000503795, 0.054147657996509224, 0.07277082899963716, 0.06854295400262345, 0.07194811500085052, 0.04628036900248844, 0.07148547999531729, 0.06051465299970005, 0.05260770599852549, 0.05164230200171005, 0.0488976909982739, 0.04197708900028374, 0.04144760000053793, 0.0310659519964247, 0.035501523001585156, 0.03954965100274421, 0.0472352819997468, 0.03594075000000885, 0.043282657003146596, 0.041589045998989604, 0.07888542299770052, 0.07613752400357043, 0.03402561000257265, 0.03721763499925146, 0.026358759998402093, 0.049896197000634857, 0.02677972499805037, 0.057379759004106745, 0.042101825994905084, 0.03989081600593636, 0.07822594600293087, 0.042355479003163055, 0.032610895003017504, 0.032807711002533324, 0.07284381700446829, 0.02887890899728518, 0.05217055500543211, 0.03990672800136963, 0.04818862899992382, 0.059414356997876894, 0.03587033299845643, 0.05952012199850287, 0.05879090500093298, 0.04983734000416007, 0.054740993000450544, 0.03298489000007976, 0.04058156700193649, 0.029498951997084077, 0.056697539999731816, 0.06145005200232845, 0.034602992003783584, 0.04549926899926504, 0.03797245500027202, 0.040333171004022006, 0.03361676799977431, 0.04597364999790443, 0.04196097499516327, 0.034056587996019516, 0.04042021700297482, 0.05231480499787722, 0.058599515999958385, 0.0799129700026242, 0.03608046600129455, 0.04029892299877247, 0.05233828999917023, 0.03652066300128354, 0.02799215000413824, 0.07322925300104544, 0.04320804900635267, 0.029960290994495153, 0.06793834899872309, 0.05853194300289033, 0.03499812399968505, 0.03141881200281205, 0.04696276300091995, 0.03242510099516949, 0.03809566899872152, 0.03656515300099272, 0.04940078499930678, 0.046909805001632776, 0.04238029699627077, 0.0328849369980162, 0.04852876499353442, 0.040025950998824555, 0.05995137900026748, 0.04450982499838574, 0.057083924002654385]
# starting iteration 4
# NN Traj [348, 176, 150, 256, 316, 166, 188, 260, 158, 368, 196, 216, 320, 336, 164, 304, 256, 282, 420, 204, 202, 158, 180, 248, 254, 186, 206, 232, 158, 244, 150, 162, 160, 198, 252, 232, 160, 130, 256, 200, 374, 212, 122, 250, 148, 152, 178, 198, 272, 208, 210, 164, 206, 182, 200, 264, 160, 220, 166, 148, 228, 182, 266, 208, 156, 166, 242, 180, 338, 136, 152, 178, 204, 226, 168, 126, 204, 186, 144, 174, 188, 254, 208, 202, 314, 150, 172, 212, 178, 208, 402, 152, 238, 196, 246, 178, 312, 154, 170, 230]
# NN Time [2.2027992329967674, 1.3496584290041937, 1.2511746649979614, 1.9258080270010396, 2.297494682003162, 1.4011027509986889, 1.4612887149996823, 1.8652982280036667, 1.1928860250045545, 2.2067909889956354, 1.6024519569982658, 1.6348459639993962, 2.3107727609967696, 2.2126650779973716, 1.206642662997183, 2.2855880289935158, 2.0508350759992027, 1.9387622610011022, 2.7747432500036666, 1.536026983994816, 1.4865079990049708, 1.2625442179996753, 1.4433376750021125, 1.7465356449974934, 1.6743906060000882, 1.5666758549996302, 1.7048464360050275, 1.9297468770018895, 1.2783846980019007, 2.063063664994843, 1.7902150690060807, 1.48318125300284, 1.280140492002829, 1.6232332970030257, 1.745448296998802, 2.138889529000153, 1.3564424059950397, 0.941764121002052, 2.0047557989964844, 1.6171188630032702, 2.3869185160001507, 1.9074787249992369, 1.0690885690055438, 2.0264085900053033, 1.300367825999274, 1.1253905150006176, 1.4126482819992816, 1.3693520669985446, 2.0307225479991757, 1.5534390920001897, 1.616009170000325, 1.3197858780040406, 1.6217127839991008, 1.3694937489999575, 1.6088452300027711, 1.9757093849984813, 1.3265166159981163, 1.74275315500563, 1.3771848640026292, 1.1222375700017437, 1.8447507720047724, 1.4301859229963156, 1.9911471720042755, 1.4906387540031574, 1.4893984380032634, 1.306276584000443, 1.6511586339984206, 1.0509032679983648, 2.430802507995395, 1.0685883480036864, 1.1775424309962546, 1.2918931480016909, 1.6031489229935687, 1.8234451669995906, 1.3474417820034432, 0.9648501389965531, 1.6704511479983921, 1.4652484999969602, 1.2575578830001177, 1.3079627839979366, 1.5615625799982809, 1.8951313349971315, 1.4507103740033926, 1.5670142020026105, 2.312560852995375, 1.1428042599945911, 1.4247600620001322, 1.7924320040037856, 1.4753199100014172, 1.384682238996902, 2.7915558620006777, 1.2578519099988625, 1.8367122230047244, 1.5772400779969757, 1.9524013420013944, 1.4599449800007278, 2.243018419998407, 1.1355832609988283, 1.4245310400001472, 1.6082047180025256]
# CNN Path [230, 152, 144, 229, 209, 150, 170, 219, 131, 199, 171, 291, 216, 280, 144, 162, 202, 286, 260, 183, 154, 140, 157, 154, 200, 178, 162, 150, 204, 207, 132, 158, 145, 157, 212, 201, 149, 129, 160, 179, 240, 171, 116, 170, 139, 135, 145, 159, 202, 159, 159, 146, 149, 237, 184, 212, 135, 258, 152, 158, 156, 151, 193, 186, 135, 266, 250, 154, 231, 131, 135, 131, 166, 129, 145, 131, 174, 179, 131, 143, 162, 164, 159, 215, 289, 152, 153, 179, 142, 168, 246, 149, 165, 141, 229, 160, 174, 136, 162, 155]
# CNN Traj [290, 168, 168, 300, 254, 164, 222, 270, 134, 280, 194, 330, 264, 346, 160, 178, 256, 382, 344, 236, 178, 158, 178, 172, 232, 206, 184, 166, 242, 254, 148, 174, 156, 184, 274, 250, 172, 144, 186, 208, 296, 200, 130, 204, 154, 152, 178, 184, 240, 192, 202, 162, 156, 326, 206, 264, 150, 286, 186, 182, 176, 178, 230, 248, 148, 324, 292, 172, 264, 146, 148, 138, 198, 136, 162, 138, 226, 238, 144, 152, 186, 192, 190, 280, 338, 170, 174, 210, 164, 214, 288, 170, 192, 162, 286, 178, 210, 150, 176, 218]
# CNN Time [4.121950959000969, 2.4739014669976314, 2.254333705001045, 4.264367643998412, 3.605567761005659, 2.5821802359932917, 3.083163796000008, 3.6505684449948603, 2.0159359399985988, 3.6912219480000203, 2.977504452006542, 5.107955277999281, 3.8440609450044576, 4.810199710998859, 2.375150357001985, 2.8396554209975875, 3.7134365969977807, 5.374689226002374, 5.293794142002298, 3.1462977999981376, 2.9073227379994933, 2.433296903000155, 2.954053813999053, 2.777429623005446, 3.6533715529949404, 3.216809267003555, 3.2877588720002677, 2.3821069140030886, 3.796705814995221, 3.7881551359969308, 2.448793587995169, 2.610754826993798, 2.741181309997046, 2.7758836049979436, 4.7229290879986365, 3.854869475006126, 2.9167685770007665, 2.20317499399971, 3.05708192900056, 3.2668319509975845, 4.540833936000126, 3.1070797560023493, 2.1224256900022738, 2.8935779680032283, 2.6327961729984963, 2.3364262960021733, 2.6886856829951284, 2.4763251449985546, 3.2651733480015537, 2.635341139997763, 3.0092442120003398, 2.689875016003498, 2.4454356639980688, 4.60895407100179, 3.4382438870015903, 3.5837015680008335, 2.466151869004534, 4.408192898001289, 3.066398479997588, 2.924130482999317, 2.7638447680001264, 2.5502520539957914, 3.6947263129986823, 3.2421087399998214, 2.320765696997114, 4.831214552999882, 4.617021992002265, 2.207500575001177, 4.298402240005089, 2.2649938089962234, 2.3035048349993303, 2.115088165002817, 2.927317444002256, 2.212804894996225, 2.4491248600024846, 2.1222120949969394, 2.896480433999386, 2.9202714470011415, 2.3340579139985493, 2.385282154995366, 2.8244400639960077, 2.61119146800047, 2.5370966019982006, 3.6080376059981063, 4.9770983300040825, 2.495409365998057, 2.729621592006879, 3.1903509399999166, 2.358411459994386, 3.125815417006379, 4.70694298500166, 2.736367004996282, 2.8657825960035552, 2.3026864290004596, 4.176635495001392, 2.6332684380031424, 3.4680435440022848, 2.326727026993467, 2.666200731997378, 2.7874691410033847]
# A Path [227, 150, 144, 196, 202, 193, 166, 226, 131, 194, 171, 192, 214, 245, 142, 154, 201, 178, 242, 187, 158, 140, 158, 153, 199, 184, 157, 150, 140, 171, 132, 154, 143, 157, 187, 208, 143, 129, 199, 171, 242, 177, 115, 170, 137, 135, 133, 159, 196, 158, 158, 146, 149, 147, 177, 197, 135, 167, 145, 134, 148, 148, 197, 173, 135, 135, 175, 154, 230, 131, 135, 131, 158, 122, 163, 128, 163, 178, 129, 133, 153, 164, 159, 165, 191, 134, 151, 170, 170, 166, 254, 152, 152, 141, 197, 161, 171, 136, 162, 162]
# A Traj [374, 222, 207, 334, 327, 303, 259, 346, 175, 314, 265, 292, 351, 387, 215, 238, 322, 313, 424, 286, 256, 208, 248, 231, 311, 307, 244, 216, 217, 277, 206, 222, 201, 247, 321, 363, 234, 181, 347, 262, 407, 262, 172, 264, 204, 199, 220, 237, 306, 246, 244, 220, 213, 220, 264, 306, 204, 245, 237, 195, 224, 254, 335, 282, 206, 188, 275, 227, 357, 190, 204, 185, 255, 179, 238, 178, 248, 272, 187, 184, 235, 254, 246, 256, 303, 191, 230, 256, 281, 277, 436, 237, 238, 216, 323, 249, 280, 198, 240, 275]
# A Time [0.07061816699570045, 0.03561878899927251, 0.03560868399654282, 0.08036649100540671, 0.0560449240001617, 0.04730196500167949, 0.050683164001384284, 0.047603803999663796, 0.03566566300287377, 0.08309137099422514, 0.04636236299847951, 0.040926813999249134, 0.05069077700318303, 0.055791013000998646, 0.037827076004759874, 0.05790038000122877, 0.05771599100262392, 0.09435293600108707, 0.0865061010044883, 0.050664098002016544, 0.04386282800260233, 0.062197267994633876, 0.03787423099856824, 0.0394925489963498, 0.0462508609998622, 0.11970190900319722, 0.05399023799691349, 0.04693971700180555, 0.041112494000117294, 0.051874250995751936, 0.049439210997661576, 0.03212486099801026, 0.0384016710013384, 0.04030989099555882, 0.056449920004524756, 0.06599484099569963, 0.04911540600005537, 0.024399866997555364, 0.07492404300137423, 0.03671874499559635, 0.10938456500298344, 0.038540469999134075, 0.03353274899563985, 0.04326756700174883, 0.042594409998855554, 0.032850046998646576, 0.04279405799752567, 0.036016707999806385, 0.03893460600374965, 0.04731934500159696, 0.03964214200095739, 0.04277049400116084, 0.03337579300568905, 0.050479965000704397, 0.0401813200005563, 0.06482292000146117, 0.032854410994332284, 0.05456081799638923, 0.040959198006021325, 0.0308768710019649, 0.037501573002373334, 0.044902601999638136, 0.054274786001769826, 0.057376698998268694, 0.05095900499873096, 0.025253388004784938, 0.04284013599681202, 0.038211708997550886, 0.060764082998503, 0.03160395699524088, 0.03608346200053347, 0.031007767000119202, 0.056647854995389935, 0.031723635001981165, 0.0380931289982982, 0.03402677999838488, 0.04247030400438234, 0.04365488600160461, 0.030421755000134, 0.02743830699910177, 0.05615469099575421, 0.04903912099689478, 0.04046323800139362, 0.04238558799988823, 0.04669607499818085, 0.02970003499649465, 0.04160853000212228, 0.0392998110037297, 0.05072656899574213, 0.050890824997622985, 0.0865952149979421, 0.04368172699469142, 0.049587899004109204, 0.03931668499717489, 0.05433915400499245, 0.038693433001753874, 0.06851091200223891, 0.029626386996824294, 0.043197765000513755, 0.07501617199886823]
# starting iteration 5
# NN Path
# NN Traj [212, 190, 188, 166, 284, 184, 148, 164, 162, 232, 178, 318, 194, 234, 230, 144, 124, 224, 254, 156, 258, 334, 166, 216, 170, 176, 168, 236, 242, 226, 322, 174, 166, 172, 602, 252, 160, 234, 176, 138, 178, 198, 252, 202, 162, 168, 156, 254, 184, 186, 312, 172, 212, 254, 234, 176, 254, 150, 300, 154, 266, 252, 206, 372, 158, 202, 234, 204, 214, 160, 202, 576, 160, 144, 406, 246, 166, 174, 204, 150, 188, 208, 312, 154, 188, 130, 244, 216, 252, 292, 308, 184, 118, 160, 140, 344, 290, 234, 224, 250]
# NN Time [1.7318937059972086, 1.4335537569932058, 1.481015829005628, 1.2245296309993137, 2.0541704220013344, 1.4664190749972477, 1.1336085560033098, 1.3133260289978352, 1.3056308459999855, 1.7602681030039093, 1.3435901560005732, 2.261394819004636, 1.3946657330016023, 1.7946401799999876, 1.6341051390045322, 1.1778619249962503, 0.9469989229983184, 1.5845646759989904, 2.088121784996474, 1.2608444840006996, 1.969210896997538, 2.211122686996532, 1.3583837909973226, 1.770012218003103, 1.3484398729997338, 1.3351294419990154, 1.3852043299993966, 1.5318532149976818, 1.8388704309982131, 1.5982249250009772, 2.4146564190014033, 1.2711292489984771, 1.3859235600029933, 1.3612371350027388, 3.6241708419984207, 1.982974253995053, 1.443185812997399, 1.5735662049992243, 1.3825470020019566, 1.1467948270001216, 1.4344114399937098, 1.6381163590049255, 1.9830864439936704, 1.8438345969989314, 1.2448430719960015, 1.3227277500045602, 1.227147245001106, 1.9331816080011777, 1.550973730001715, 1.4519829899945762, 2.229411315995094, 1.4160848210012773, 1.6417998309989343, 1.9293250359987724, 1.9279892179984017, 1.360531059995992, 1.99507791200449, 1.2646567350020632, 1.9889172780021909, 1.222799565999594, 1.9458842120002373, 1.937217958002293, 1.516264065001451, 2.4854955570044694, 1.3630947579949861, 1.5332191120032803, 1.8487608170034946, 1.5912401040041004, 1.6033044950017938, 1.3078651609976077, 1.6031437489946256, 3.515783171998919, 1.2799995390014374, 1.1814900530007435, 2.7752617849982926, 2.0488686169992434, 1.277882377995411, 1.3345362229956663, 1.4688696429948322, 1.2639524319965858, 1.4412028690057923, 1.7220611199954874, 2.326739140000427, 1.229206288997375, 1.5049531369950273, 0.9448685579991434, 1.7980523830046877, 1.6246618699951796, 1.9249063589959405, 2.128387037999346, 2.0297466589981923, 1.5331655710033374, 1.0335912729933625, 1.2292772520013386, 1.082191690999025, 2.3991355129983276, 2.100800722000713, 1.7480480480007827, 1.6459673539939104, 1.8535421679989668]
# CNN Path [136, 161, 158, 146, 193, 143, 232, 165, 170, 187, 154, 203, 157, 191, 154, 140, 122, 220, 233, 147, 338, 204, 163, 143, 180, 195, 145, 150, 174, 169, 214, 206, 150, 147, 295, 325, 147, 149, 153, 129, 156, 159, 172, 203, 148, 165, 169, 198, 160, 150, 148, 150, 127, 144, 180, 147, 201, 149, 188, 132, 152, 211, 143, 282, 140, 169, 176, 160, 130, 163, 161, 222, 176, 128, 248, 175, 146, 158, 168, 136, 180, 235, 224, 144, 213, 118, 177, 214, 166, 220, 197, 174, 136, 147, 124, 192, 191, 175, 172, 190]
# CNN Traj [144, 176, 172, 162, 240, 168, 292, 196, 200, 212, 170, 240, 174, 218, 174, 146, 124, 270, 312, 168, 496, 278, 184, 154, 216, 222, 158, 164, 196, 208, 260, 234, 164, 170, 412, 446, 166, 164, 168, 144, 186, 176, 202, 242, 164, 200, 194, 242, 186, 174, 164, 168, 136, 164, 228, 160, 260, 166, 234, 150, 174, 230, 164, 406, 154, 194, 214, 202, 144, 186, 168, 300, 198, 134, 364, 192, 160, 188, 190, 150, 204, 278, 262, 166, 250, 124, 198, 248, 208, 274, 292, 214, 148, 156, 132, 258, 230, 212, 204, 218]
# CNN Time [2.2101541810043273, 2.637010778998956, 2.5148024830050417, 2.4190002270042896, 3.396242760005407, 2.3799349370019627, 4.162605943005474, 2.822780873000738, 2.906385271002364, 3.18623689100059, 2.4163168919985765, 3.5311093279960915, 2.657739037000283, 3.367911016001017, 2.515011299001344, 2.3102042370010167, 1.7405141150011332, 3.8562724539951887, 4.378857431001961, 2.558615517002181, 6.022772251999413, 3.478601411996351, 2.697987617000763, 2.3514243499957956, 3.074113174996455, 3.3259353060057038, 2.6350858119985787, 2.426415112000541, 2.691685393001535, 2.8859519450052176, 3.805203438998433, 3.6894731450011022, 2.4064747049997095, 2.3802964120040997, 5.2067420759995, 5.970456706003461, 2.6696785789972637, 2.320921177000855, 2.379995045994292, 2.4055494209969766, 3.02250561099936, 2.721476475999225, 3.123631636000937, 3.8403694999942672, 2.6347766790058813, 2.8287294629990356, 2.923654164005711, 3.6146745799997007, 2.8577235659977305, 2.7448356649983907, 2.570392150999396, 2.5478749419999076, 2.144086560001597, 2.4167775850000908, 3.1480459779995726, 2.417114740004763, 3.7557246459982707, 2.5084239709976828, 2.9786898179954733, 2.1974716910044663, 2.602295091994165, 3.724559860995214, 2.5673090419950313, 5.213749285998347, 2.275853310005914, 2.815390806994401, 3.298990324001352, 2.987071087998629, 2.513460675996612, 2.5933449000003748, 2.5926799070002744, 4.1394474179978715, 3.119122399999469, 1.907888608999201, 5.067462979000993, 3.0553873230019235, 2.365879789002065, 2.7746517429986852, 2.816144748001534, 2.149704080999072, 3.1956593799986877, 4.206670930994733, 3.8237263439950766, 2.403771751000022, 3.8073872759996448, 1.751524725004856, 3.4297701160030556, 3.7890257309991284, 3.0357705610003904, 3.7582790619999287, 3.4080161890015006, 3.141221568999754, 2.2923136629979126, 2.4817621479960508, 1.9796879350033123, 3.436639658000786, 3.3001433519966668, 3.233613862998027, 3.1434947789966827, 3.1697211589998915]
# A Path [136, 157, 158, 145, 188, 142, 123, 162, 142, 184, 153, 205, 155, 166, 155, 140, 122, 179, 222, 142, 265, 198, 163, 143, 152, 193, 145, 146, 173, 170, 249, 197, 152, 147, 274, 188, 147, 149, 141, 136, 152, 159, 158, 148, 148, 164, 169, 171, 156, 144, 148, 150, 181, 144, 318, 146, 196, 148, 184, 132, 149, 191, 144, 250, 140, 160, 173, 150, 140, 163, 153, 284, 176, 128, 222, 171, 145, 157, 168, 135, 155, 168, 207, 138, 213, 118, 175, 179, 165, 132, 186, 160, 140, 147, 124, 192, 185, 171, 173, 172]
# A Traj [193, 233, 214, 220, 301, 220, 187, 248, 226, 282, 224, 328, 233, 253, 244, 193, 155, 278, 389, 226, 479, 345, 248, 210, 227, 295, 224, 204, 260, 272, 386, 293, 229, 225, 455, 299, 230, 208, 230, 189, 244, 237, 236, 231, 220, 253, 253, 264, 235, 223, 218, 228, 326, 219, 551, 213, 350, 222, 299, 200, 224, 302, 226, 435, 205, 240, 282, 248, 216, 247, 224, 587, 275, 175, 417, 254, 201, 256, 250, 204, 241, 271, 323, 208, 348, 157, 277, 304, 278, 186, 318, 244, 220, 202, 175, 317, 290, 287, 290, 252]
# A Time [0.04449745299643837, 0.0499301279996871, 0.030559844999515917, 0.0354758449975634, 0.04056366799341049, 0.04552153999975417, 0.05028945500089321, 0.03354224099894054, 0.05221825400076341, 0.04771601199900033, 0.044496802998764906, 0.07260918799875071, 0.040860060005798005, 0.04043629400257487, 0.04402799600211438, 0.03447472400148399, 0.020043145996169187, 0.04546789699816145, 0.09500543400645256, 0.050246030994458124, 0.10467027599952416, 0.06986970800062409, 0.040680590995179955, 0.03835360500670504, 0.0329648469996755, 0.050207001993840095, 0.034167395999247674, 0.029536854999605566, 0.041167174000293016, 0.0729934740011231, 0.09081981800409267, 0.041368320999026764, 0.03939165799965849, 0.039837293996242806, 0.09427890699589625, 0.044115978002082556, 0.04538470499392133, 0.02494836500409292, 0.037714707003033254, 0.030131371997413225, 0.04325044799770694, 0.05431523500010371, 0.05986156799917808, 0.07682564000424463, 0.03787324999575503, 0.04072606399859069, 0.0391911490005441, 0.039919814000313636, 0.05572232099802932, 0.0423446509958012, 0.038163180004630703, 0.04028728700359352, 0.05871944899990922, 0.0477580040023895, 0.202177605999168, 0.03479230599623406, 0.06428913399577141, 0.05074831199453911, 0.044198663999850396, 0.036942740000085905, 0.03617479099921184, 0.04943173200445017, 0.0407507580021047, 0.09610760800569551, 0.03497853400040185, 0.03526463299931493, 0.06048700000246754, 0.03905233700061217, 0.0468341409941786, 0.048260819996357895, 0.03965395600243937, 0.11315015100262826, 0.04635277100169333, 0.03200141400157008, 0.06743575599830365, 0.03643822899903171, 0.027300309993734118, 0.04081176999898162, 0.04102819800027646, 0.05218553799932124, 0.05725372300366871, 0.052269101004640106, 0.06302268600120442, 0.02876451699557947, 0.05879529999947408, 0.031920091998472344, 0.04976333100057673, 0.05436643100256333, 0.05668076599977212, 0.03161362399987411, 0.053109335000044666, 0.0405068609979935, 0.047271130002627615, 0.028409696999005973, 0.027190622000489384, 0.055082999999285676, 0.04414089400233934, 0.057515104999765754, 0.058819163001317065, 0.04812885799765354]
# starting iteration 6
# NN Path
# NN Traj [146, 200, 216, 252, 148, 196, 176, 340, 348, 180, 148, 212, 142, 196, 210, 190, 184, 298, 144, 138, 190, 200, 134, 144, 160, 170, 270, 156, 256, 232, 170, 244, 148, 186, 236, 316, 220, 212, 276, 158, 126, 190, 152, 374, 210, 142, 184, 214, 282, 140, 176, 154, 164, 254, 196, 202, 154, 160, 242, 192, 208, 214, 176, 202, 112, 182, 206, 186, 170, 150, 276, 300, 194, 310, 226, 142, 150, 182, 294, 162, 232, 266, 226, 210, 218, 244, 172, 290, 172, 238, 396, 118, 214, 186, 234, 184, 182, 246, 176, 152]
# NN Time [1.1149351420026505, 1.4811068419949152, 1.5165383339990512, 1.9384041290031746, 1.1467902410004172, 1.4381154779985081, 1.4291763910005102, 2.500877730999491, 2.224375024998153, 1.3764177769990056, 1.1850680150018889, 1.6451054219942307, 1.1388070329994662, 1.4917024899987155, 1.572938882993185, 1.4913743370052543, 1.5184605800022837, 2.157181976006541, 1.1021235680018435, 1.0911988579973695, 1.5533793559952755, 1.4720166700062691, 1.129858364998654, 1.0594572889967822, 1.3727566589950584, 1.3737552339953254, 1.9196749050024664, 1.2919927039984032, 1.9292794310022146, 1.7457893769969814, 1.4582073920028051, 1.8872339699955774, 1.1712532390010892, 1.4543080009971163, 1.7680023229986546, 2.096813962001761, 1.2957369950017892, 1.7124924290037598, 2.017736394001986, 1.191560876002768, 1.1812019669960137, 1.5108307269983925, 1.1311051459997543, 2.2904118439982994, 1.5790212790016085, 1.1655253260032623, 1.3912572900007945, 1.6705253249965608, 2.1377452599990647, 1.1760041560046375, 1.4473163100046804, 1.146689712004445, 1.231930679998186, 1.7184390539987362, 1.4184770789943286, 1.6632612530011102, 1.2980123849993106, 1.2224357969971607, 1.8653242150030565, 1.4298148329980904, 1.4636210929966182, 1.7967133519996423, 1.2393788370027323, 1.549794546001067, 0.8944337189968792, 1.3612778059978154, 1.6282998230017256, 1.3992377079994185, 1.3589007279952057, 1.2963516200034064, 1.9453079730010359, 2.2174458500012406, 1.5875909039968974, 2.1339393820016994, 1.7762733609997667, 1.179785827996966, 1.2066435149972676, 1.4527653620025376, 1.9922190199940815, 1.3705445149971638, 1.7693909290028387, 1.8863785060020746, 1.839578225997684, 1.6042604169997503, 1.7565246619997197, 1.5666319630036014, 1.3674310050046188, 2.1612405329942703, 1.3481402819961659, 1.6560824519983726, 2.72246604400425, 0.8784658040021895, 1.536717884002428, 1.5125418300012825, 1.6845413140035816, 1.341191199993773, 1.2980386229974101, 1.90287083399744, 1.2444833980043768, 1.2667808179976419]
# CNN Path [135, 145, 151, 198, 143, 153, 156, 208, 207, 149, 133, 157, 177, 158, 219, 156, 158, 205, 183, 127, 181, 152, 135, 134, 134, 161, 177, 151, 191, 191, 183, 183, 141, 176, 158, 197, 159, 157, 194, 145, 179, 145, 145, 134, 192, 134, 155, 170, 231, 131, 143, 146, 138, 188, 203, 160, 138, 184, 246, 148, 155, 186, 149, 174, 111, 159, 160, 172, 149, 137, 196, 201, 146, 203, 160, 115, 160, 162, 164, 132, 177, 194, 217, 172, 192, 169, 168, 216, 168, 197, 408, 115, 208, 150, 163, 149, 141, 230, 146, 150]
# CNN Traj [150, 166, 168, 238, 158, 162, 176, 262, 268, 172, 142, 174, 214, 186, 260, 172, 170, 242, 218, 130, 204, 164, 138, 142, 152, 210, 204, 170, 252, 248, 222, 216, 154, 224, 184, 276, 180, 178, 228, 164, 206, 166, 164, 158, 222, 144, 168, 190, 276, 140, 168, 164, 154, 244, 244, 190, 150, 206, 332, 158, 178, 240, 166, 196, 114, 178, 176, 198, 168, 154, 254, 234, 164, 246, 182, 122, 174, 200, 206, 152, 208, 234, 276, 188, 276, 194, 196, 272, 206, 248, 618, 118, 266, 166, 198, 158, 162, 290, 152, 194]
# CNN Time [2.2531712709969725, 2.68887854499917, 2.323070287995506, 3.462233958998695, 2.3181391499965684, 2.4211861840012716, 2.6106587309986935, 4.145914416003507, 3.5182748300067033, 2.6847495820038603, 2.0701015449958504, 2.5418479569998453, 2.920845141998143, 2.872441034996882, 3.7209890160011128, 2.697945407999214, 2.7637551719963085, 3.553757328998472, 2.8042781940021086, 1.9595503689997713, 3.303078215001733, 2.5046079239982646, 2.066357688003336, 2.1182428739994066, 2.4064575009979308, 2.7978699000013876, 3.042420931997185, 2.7911358429992106, 3.724931992997881, 3.6191866750014015, 3.223847048000607, 3.2435151529934956, 2.3760645860020304, 3.1952832469978603, 2.9118310189951444, 3.25472532799904, 2.633994279000035, 2.6013931540001067, 3.647335462999763, 2.346596508003131, 3.1993014000036055, 2.7195605500019155, 2.3794298419961706, 2.388540756001021, 3.4273303610025323, 2.298682740001823, 2.8371589189991937, 3.088824086000386, 3.9742962010059273, 2.203048099996522, 2.5154155749987694, 2.4084279790040455, 2.263793631995213, 2.9475570040012826, 3.230900151997048, 2.7916285729952506, 2.3434383010026067, 3.069099611006095, 4.656929138996929, 2.4421627260016976, 2.825427881005453, 3.466584891000821, 2.501660940994043, 2.8684969460009597, 1.6743089290030184, 2.7157667970022885, 2.7180007839997415, 3.1678285020025214, 2.481655273004435, 2.3880968330049654, 3.4039784029955626, 3.6466976200026693, 2.546758240998315, 3.1436925559974043, 2.782386929000495, 1.8701692190006725, 2.512443555002392, 3.0044330739983707, 3.046715520998987, 2.258136149001075, 2.8144807559947367, 3.5057239570014644, 3.8315123499996844, 2.895725881004182, 3.5629063689993927, 2.6970567269963794, 2.825887111001066, 4.05135087799863, 3.1410043420037255, 3.4636315749958158, 7.264295638000476, 1.5959760010009632, 3.572325301996898, 2.6108212060062215, 2.958364662998065, 2.393289677995199, 2.5044039729982615, 4.2478509309949, 2.1864486839986057, 2.795851668997784]
# A Path [135, 150, 132, 200, 136, 156, 156, 221, 185, 148, 133, 156, 170, 160, 192, 155, 156, 201, 179, 127, 180, 150, 144, 133, 134, 148, 176, 144, 177, 184, 183, 178, 192, 176, 158, 170, 153, 157, 199, 139, 168, 163, 143, 169, 173, 134, 154, 157, 209, 131, 148, 145, 162, 177, 189, 162, 138, 181, 203, 148, 156, 182, 147, 174, 111, 159, 160, 172, 146, 137, 191, 203, 146, 200, 144, 115, 144, 152, 163, 135, 181, 231, 200, 170, 205, 166, 146, 205, 167, 194, 248, 115, 203, 152, 165, 148, 137, 209, 146, 132]
# A Traj [204, 234, 207, 325, 197, 227, 236, 366, 289, 238, 192, 227, 248, 264, 300, 230, 229, 325, 271, 175, 279, 218, 209, 185, 209, 232, 289, 213, 294, 297, 287, 281, 285, 305, 241, 263, 237, 238, 323, 199, 266, 247, 211, 271, 279, 196, 218, 232, 330, 193, 243, 217, 253, 275, 303, 264, 211, 265, 365, 213, 254, 303, 211, 259, 149, 237, 254, 272, 221, 220, 318, 328, 222, 326, 213, 168, 202, 248, 279, 212, 287, 387, 313, 246, 346, 245, 221, 334, 268, 311, 410, 141, 327, 238, 285, 204, 215, 338, 199, 201]
# A Time [0.03786751100415131, 0.040371073002461344, 0.041894098998454865, 0.04628341400530189, 0.03198538099968573, 0.04007523700420279, 0.03669220300071174, 0.07410292299755383, 0.04419955399498576, 0.03870122200169135, 0.03971343799639726, 0.03059276299609337, 0.0358567570001469, 0.05306914900575066, 0.05162957299762638, 0.04027401700295741, 0.03086629199970048, 0.04734099699999206, 0.04127791400242131, 0.04525191100401571, 0.05912800000078278, 0.0505961090020719, 0.0338247620020411, 0.06594135200430173, 0.03618428900517756, 0.05202605199883692, 0.041186697999364696, 0.03036705699923914, 0.05603425499430159, 0.0479708419952658, 0.058814239004277624, 0.04932733099849429, 0.057926223998947535, 0.06848752299993066, 0.039820705002057366, 0.05127776000153972, 0.0448667189994012, 0.034822952999093104, 0.048342895999667235, 0.03260853599931579, 0.04578517800109694, 0.041522814994095825, 0.03101451299880864, 0.05137504999584053, 0.043555987002037, 0.04053794799983734, 0.05392430300707929, 0.04455017599684652, 0.05910811400099192, 0.03786500400019577, 0.05768575399997644, 0.03777574400010053, 0.040561504996730946, 0.057067731002462097, 0.050537934999738354, 0.05308939699898474, 0.044160234996525105, 0.04804736499499995, 0.07938856900000246, 0.03392071399866836, 0.048853686996153556, 0.06342391399812186, 0.029776835996017326, 0.034815309998521116, 0.027736692005419172, 0.03250991400273051, 0.04637848899437813, 0.04007335999631323, 0.04138940799748525, 0.04257640500145499, 0.06167567399825202, 0.05324266399838962, 0.035929852005210705, 0.09784056500211591, 0.04952715399849694, 0.03209116899961373, 0.0367989270016551, 0.036450633000640664, 0.05603955099650193, 0.0447045659966534, 0.056116098996426445, 0.07133448300010059, 0.06492134099971736, 0.043981292998068966, 0.06168718499975512, 0.03994407699792646, 0.04217830800189404, 0.06577156799903605, 0.03999322800518712, 0.08106746400153497, 0.08633195599395549, 0.023085134002030827, 0.0685209430012037, 0.03749219999735942, 0.048772641996038146, 0.03270064500247827, 0.03453439699660521, 0.06070374599948991, 0.029329254997719545, 0.0620462349979789]
# starting iteration 7
# NN Path
# NN Traj [212, 464, 170, 278, 146, 202, 148, 146, 234, 230, 204, 196, 170, 178, 212, 234, 110, 248, 142, 184, 278, 236, 168, 188, 380, 324, 244, 168, 332, 156, 160, 254, 144, 160, 210, 178, 220, 152, 160, 192, 148, 172, 314, 206, 190, 194, 162, 146, 168, 154, 170, 148, 194, 262, 132, 266, 160, 208, 148, 142, 184, 234, 258, 204, 208, 252, 136, 152, 190, 180, 218, 190, 320, 206, 218, 192, 134, 148, 162, 232, 136, 136, 176, 182, 180, 304, 190, 198, 192, 158, 178, 136, 170, 172, 186, 138, 242, 236, 160, 232]
# NN Time [1.5754261949987267, 3.336326790995372, 1.2986436310020508, 1.9172266490058973, 1.1771187009944697, 1.5470458929994493, 1.262703751999652, 1.1490406919983798, 1.7547465309980907, 1.6314402149946545, 1.5534527290001279, 1.3502708060041186, 1.374598877999233, 1.3217835309988004, 1.6978266639998765, 1.747476328004268, 0.8965261089979322, 1.7800073300022632, 1.135191499000939, 1.4998046699984116, 1.8583269370064954, 1.7729348189968732, 1.3651930499981972, 1.4208261499952641, 2.6178016330013634, 2.380779626000731, 1.6476998149955762, 1.3194611029975931, 2.3312305829967954, 1.1563692679992528, 1.2486832350041368, 1.8846347149956273, 1.1256364060027408, 1.2282854430013685, 1.481729589002498, 1.3592747489965404, 1.5788376969940146, 1.2732560570002533, 1.1638433190018986, 1.4393587049999041, 1.1607202900049742, 1.379754128996865, 1.997019784997974, 1.5277147810047609, 1.4411157300055493, 1.3835800729939365, 1.1348314839997329, 1.1626543829988805, 1.2608721599972341, 1.2244211949946475, 1.3367154770021443, 1.2879862420013524, 1.5422616239957279, 2.000302214997646, 1.0211651450008503, 1.9803685909937485, 1.3385964589979267, 1.5606801620015176, 1.2508726400046726, 1.0007301090008696, 1.2450511169954552, 1.9419121989994892, 1.7693614539966802, 1.5117936700044083, 1.5733792989994981, 2.064589817004162, 1.06404575099441, 1.2382377999965684, 1.448909195001761, 1.3585062109996215, 1.638282485997479, 1.6085974689995055, 2.3296008839970455, 1.6760424120002426, 1.6011429450009018, 1.6466537109954515, 1.0999795950046973, 1.095948448004492, 1.3639748810019228, 1.8372712100026547, 1.0504212580053718, 1.1474866160060628, 1.4008254630025476, 1.4010239599956549, 1.4244074410016765, 2.1907156230008695, 1.6808610760053853, 1.4584251729975222, 1.4625113130023237, 1.124634095998772, 1.3310427169999457, 1.0430530250014272, 1.284545968999737, 1.3196609019942116, 1.5488269589986885, 1.0346394520020112, 1.6147548129956704, 1.623109811996983, 1.2488083909993293, 1.728096805003588]
# CNN Path [177, 342, 164, 191, 125, 149, 133, 174, 159, 189, 158, 158, 155, 134, 275, 183, 108, 179, 137, 162, 182, 172, 181, 154, 273, 203, 177, 197, 149, 146, 135, 179, 133, 166, 142, 142, 151, 149, 137, 140, 130, 148, 203, 150, 229, 146, 148, 155, 141, 139, 261, 150, 164, 200, 130, 255, 142, 180, 136, 130, 132, 195, 172, 171, 149, 412, 131, 167, 164, 156, 194, 160, 205, 171, 189, 155, 124, 132, 143, 163, 125, 135, 149, 163, 176, 253, 175, 167, 145, 149, 123, 124, 139, 167, 170, 119, 143, 163, 145, 197]
# CNN Traj [210, 424, 190, 244, 148, 170, 146, 206, 200, 244, 178, 204, 162, 146, 324, 222, 110, 208, 154, 182, 230, 226, 214, 182, 356, 252, 204, 230, 166, 156, 156, 228, 134, 194, 150, 162, 176, 172, 164, 150, 150, 166, 288, 180, 274, 172, 174, 168, 160, 156, 344, 168, 182, 236, 132, 306, 160, 202, 144, 136, 136, 222, 202, 196, 166, 570, 134, 200, 196, 180, 244, 180, 254, 186, 250, 186, 130, 136, 158, 188, 132, 140, 168, 170, 210, 296, 184, 194, 156, 164, 136, 128, 154, 174, 186, 126, 166, 192, 154, 244]
# CNN Time [2.8551359349949053, 6.064577789002215, 2.872663202993863, 3.1324545230017975, 2.034793136997905, 2.577578871998412, 2.2976538529983372, 2.888164873998903, 2.74046450300375, 3.4004971309987013, 2.581954572997347, 2.5778944840058102, 2.8037395360006485, 2.259713516003103, 4.641530842993234, 3.154780775003019, 1.5345199570001569, 3.2510183039994445, 2.4233820069930516, 2.8041783119988395, 2.9199947390006855, 2.93169881200447, 3.3840087040007347, 2.768172695999965, 5.009917176001181, 3.76318300699495, 3.0456492899975274, 3.2612001599991345, 2.6651505249974434, 2.383811285995762, 2.2301275040008477, 3.2794513530025142, 1.861158225001418, 2.558983574999729, 2.3571275970025454, 2.3616339080035686, 2.7032535030011786, 2.81773910800257, 2.1697992449990124, 2.3476024880001205, 2.231797918997472, 2.5013874169962946, 4.013388671002758, 2.61229881500185, 4.087144181001349, 2.6875054289994296, 2.7116194530026405, 2.4924810390002676, 2.0413856799932546, 2.3161762219970115, 4.759205219997966, 2.51064109300205, 3.0007747909985483, 3.576725113001885, 1.8941297729979851, 4.394356471995707, 2.4631747719977284, 2.8950516340046306, 2.1752127229992766, 2.149483399996825, 2.071520875004353, 3.2606715129950317, 2.7977014019998023, 2.7967861580036697, 2.6598376980036846, 7.082856476998131, 2.1368903850016068, 2.831740637004259, 2.7880054819979705, 2.4785531890011043, 3.2983912820054684, 2.9841862190005486, 3.689757894993818, 2.7712325880056596, 3.236111551996146, 2.7215346270022565, 2.1097945860019536, 1.9951008619973436, 2.3214141719945474, 2.875163112003065, 2.179631792998407, 2.1776381800009403, 2.46772718399734, 2.671910762997868, 2.938783274999878, 4.05967489300383, 2.997166898996511, 2.9256380980004906, 2.3999262810029904, 2.209495645998686, 2.114637781000056, 1.926434042994515, 2.2410515939991456, 2.575210650000372, 2.9213277020026, 1.865340094002022, 2.393091714999173, 2.815965974004939, 2.268281291006133, 3.331038060001447]
# A Path [177, 322, 156, 189, 123, 156, 138, 134, 171, 183, 158, 157, 155, 134, 185, 175, 108, 172, 143, 165, 181, 167, 143, 154, 268, 203, 175, 187, 149, 146, 140, 181, 133, 166, 142, 140, 159, 149, 137, 140, 134, 146, 206, 150, 192, 145, 148, 135, 141, 139, 247, 149, 165, 200, 130, 231, 140, 163, 137, 130, 132, 180, 170, 164, 147, 243, 131, 139, 156, 179, 194, 160, 204, 168, 168, 146, 124, 132, 140, 158, 125, 135, 148, 163, 154, 219, 175, 160, 160, 146, 123, 124, 139, 167, 170, 119, 160, 156, 142, 174]
# A Traj [270, 515, 248, 311, 180, 248, 213, 196, 255, 326, 235, 256, 220, 200, 292, 284, 137, 270, 220, 255, 284, 275, 232, 246, 465, 347, 269, 276, 232, 198, 220, 336, 165, 246, 207, 208, 240, 249, 214, 196, 217, 213, 377, 239, 313, 229, 233, 206, 199, 207, 414, 224, 259, 314, 169, 365, 216, 243, 201, 176, 168, 269, 257, 241, 228, 395, 172, 216, 229, 288, 327, 251, 343, 242, 261, 229, 182, 176, 210, 253, 177, 192, 224, 232, 242, 346, 254, 248, 234, 202, 190, 166, 206, 230, 256, 158, 270, 232, 204, 269]
# A Time [0.050531716995465104, 0.23455288200057112, 0.03977635900082532, 0.07589692100009415, 0.036470442995778285, 0.039032915999996476, 0.03729295900120633, 0.029906997995567508, 0.04388091999862809, 0.06471825499465922, 0.04032361899589887, 0.05539827099710237, 0.035116418002871796, 0.03135401000326965, 0.05619480799941812, 0.04674162199808052, 0.022096959997725207, 0.045198447005532216, 0.06251419299951522, 0.043601479002973065, 0.050777611999365035, 0.055914060001668986, 0.04764662699744804, 0.04367006099346327, 0.12137611000071047, 0.07626634200278204, 0.03947034700104268, 0.04422362699551741, 0.0471987370037823, 0.034515993000240996, 0.046360355998331215, 0.0651923559998977, 0.017581664003955666, 0.03539825600455515, 0.043514811994100455, 0.028869344998383895, 0.04970002699701581, 0.04443005400389666, 0.03909466000186512, 0.027286999000352807, 0.04221708600380225, 0.03599352800665656, 0.10266916000546189, 0.03962358099670382, 0.06201037999562686, 0.037677610001992434, 0.055570434997207485, 0.043382839001424145, 0.028179722001368646, 0.0352031389993499, 0.07826448600098956, 0.04167796699766768, 0.06175006100238534, 0.04671906099974876, 0.027843258998473175, 0.07001252399641089, 0.04542425299587194, 0.04444617699482478, 0.03096443400136195, 0.027229748004174326, 0.022624878001806792, 0.03680260399414692, 0.03983225399861112, 0.041831567003100645, 0.039433779995306395, 0.08490565900137881, 0.026728138996986672, 0.051449044003675226, 0.034147389997087885, 0.058189980998577084, 0.0569877070010989, 0.051290770003106445, 0.06625866000103997, 0.04196164799941471, 0.06162099899665918, 0.04235526199772721, 0.034618507997947745, 0.023962669001775794, 0.03633897200052161, 0.03832576499553397, 0.028912580004543997, 0.030854280994390137, 0.03547737099870574, 0.03618219800409861, 0.03579009399982169, 0.058342871998320334, 0.04423345900431741, 0.038824843999464065, 0.032981878001010045, 0.023222429997986183, 0.03900691899616504, 0.026825471002666745, 0.03159118499752367, 0.036084988998482004, 0.04439543299668003, 0.025249803999031428, 0.07893754999531666, 0.04941809799493058, 0.032239012005447876, 0.049319025994918775]
# starting iteration 8
# NN Traj [172, 254, 266, 322, 172, 224, 282, 140, 202, 242, 354, 166, 294, 216, 156, 182, 142, 200, 178, 174, 214, 214, 154, 178, 220, 154, 382, 202, 146, 192, 228, 230, 176, 188, 196, 220, 158, 216, 288, 162, 180, 282, 260, 120, 230, 248, 168, 152, 224, 202, 140, 182, 132, 252, 262, 252, 168, 174, 238, 128, 216, 166, 154, 232, 184, 240, 240, 190, 168, 234, 224, 224, 144, 120, 130, 202, 224, 134, 194, 202, 172, 230, 142, 176, 162, 330, 190, 194, 234, 192, 154, 156, 158, 198, 206, 168, 176, 158, 166, 222]
# NN Time [1.2684204199977103, 1.8228114590019686, 1.5331745039948146, 2.3316027199980454, 1.3455352519958979, 1.6307125449966406, 2.2331789450036013, 1.1197107429979951, 1.4146612140029902, 2.002509171004931, 2.4400168040010612, 1.252838675995008, 2.105098956999427, 1.6640191420010524, 1.3469121119996998, 1.4517710789950797, 1.1547975200010114, 1.5181056450019241, 1.3986370980055653, 1.247902969000279, 1.7834685240013641, 1.7724245599965798, 1.184269358003803, 1.3679420749976998, 1.7474894680053694, 1.216215081003611, 2.5212978390045464, 1.521372285002144, 1.130177585000638, 1.5787158399980399, 1.7434368159956648, 1.971135735999269, 1.477967731996614, 1.4121514660000685, 1.5490313050031546, 1.7353673290053848, 1.26297253100347, 1.6154227829974843, 2.1172996000022977, 1.2577777989936294, 1.4135609849981847, 2.0910920030000852, 1.9000767950055888, 0.8760685019951779, 1.850052247995336, 1.8058516539967968, 1.2899275890013087, 1.2370926229996257, 1.6833438760004356, 1.5808868130043265, 1.1945615810036543, 1.4352423860036652, 1.155885929998476, 2.035421086999122, 2.1274853789946064, 1.8163081079983385, 1.2929659059955156, 1.4133630900032585, 1.8945118489937158, 1.1016036119981436, 1.5874790320012835, 1.3362761249954929, 1.1966663030034397, 1.576974584000709, 1.481250042001193, 2.049047978995077, 1.715135852995445, 1.4049845879999339, 1.3046532940061297, 1.8441160200018203, 1.7206598409975413, 1.6537260629993398, 1.201082886000222, 0.9574259549990529, 1.1489067939983215, 1.4501126239993027, 1.7292896229992039, 0.9086505439991015, 1.5726451640002779, 1.5577079430004233, 1.3258341590044438, 1.7318455549975624, 1.179254218004644, 1.441342182995868, 1.1950698860018747, 2.2136275400043814, 1.5891690089993062, 1.5451006090006558, 1.8456328259999282, 1.4044872679951368, 1.3034479979978641, 1.116914503996668, 1.0063585300013074, 1.5087586180015933, 1.7098756300038076, 1.4622240249955212, 1.3154739849996986, 1.2220294680009829, 1.3442675949991099, 1.871124516997952]
# CNN Path [166, 197, 190, 135, 146, 181, 221, 130, 154, 238, 268, 143, 175, 187, 141, 157, 126, 213, 197, 175, 155, 172, 147, 144, 183, 133, 233, 181, 137, 188, 157, 167, 152, 149, 162, 213, 160, 148, 198, 162, 170, 199, 185, 121, 179, 194, 131, 142, 190, 194, 137, 149, 152, 176, 272, 176, 276, 144, 191, 152, 176, 159, 123, 169, 165, 167, 188, 143, 156, 236, 157, 174, 167, 120, 123, 152, 182, 137, 188, 173, 143, 155, 159, 156, 144, 220, 142, 150, 228, 152, 198, 131, 128, 170, 204, 157, 143, 139, 152, 182]
# CNN Traj [178, 244, 224, 150, 164, 204, 272, 138, 182, 292, 412, 156, 210, 212, 154, 176, 142, 272, 244, 208, 184, 198, 166, 152, 202, 144, 306, 212, 156, 220, 176, 186, 178, 154, 188, 302, 194, 182, 250, 178, 204, 242, 210, 122, 198, 222, 136, 162, 236, 256, 142, 180, 164, 198, 328, 216, 360, 164, 232, 180, 214, 188, 126, 184, 176, 194, 208, 170, 174, 308, 180, 190, 192, 128, 130, 178, 210, 148, 222, 200, 154, 182, 178, 182, 158, 290, 160, 174, 288, 166, 226, 138, 130, 206, 252, 168, 160, 148, 172, 212]
# CNN Time [2.6704374269975233, 3.505839064004249, 2.911259936998249, 2.180455597997934, 2.409049000001687, 3.0501074549974874, 3.911407332001545, 2.140579514998535, 2.538729357002012, 3.9980211109941592, 5.172530140996969, 2.43108099699748, 3.491170195004088, 3.3437694359963643, 3.0644980069992016, 2.421026341995457, 2.085454041000048, 4.194453367999813, 3.369813042001624, 2.8376835280068917, 2.4433241939987056, 3.1427288909981144, 2.3838865059951786, 2.4056971300014993, 3.2235172240034444, 2.1680799390014727, 4.375665706000291, 3.24519988099928, 2.1532423489989014, 3.376121566994698, 3.0144240820009145, 2.9419734260009136, 2.7443936109993956, 2.97678118100157, 3.026735430001281, 3.964888560003601, 2.863421592002851, 2.748570956995536, 3.562310161003552, 2.796323886999744, 2.7517923710038303, 3.767676917996141, 2.9895964870011085, 1.675749863999954, 3.0768044409996946, 3.2364617380007985, 2.0801553329947637, 2.618228719999024, 3.415536095002608, 3.650174030000926, 2.1564223910027067, 2.653540080995299, 2.3848935209971387, 2.779957074999402, 4.877344479005842, 3.144529720004357, 4.59780621399841, 2.395530827001494, 3.166151600999001, 2.6472506069985684, 3.054132056000526, 2.7645992420002585, 1.8952519070007838, 2.738783444001456, 2.6621919449971756, 3.2637458780009183, 2.9877347020010347, 2.4631866339987027, 2.5562974080021377, 4.328842538998288, 2.641174113996385, 2.769536836000043, 2.7568086859973846, 1.8502386420004768, 2.008952367999882, 2.4433885460020974, 3.1745433039977797, 2.0117158380016917, 3.197861909000494, 2.775255474000005, 2.166439321998041, 2.680792138999095, 2.4367987169971457, 2.778845670996816, 2.000644123996608, 3.9917754990019603, 2.336132201999135, 2.532640290002746, 4.181219568003144, 2.542870495002717, 3.327081368996005, 2.0558502189960564, 1.8063579019944882, 3.120889999998326, 3.6197153700049967, 2.6810765159971197, 2.3571304270008113, 2.5641484730003867, 2.6175722379994113, 3.060543380997842]
# A Path [136, 139, 196, 137, 145, 194, 217, 130, 148, 185, 263, 143, 157, 187, 141, 155, 119, 206, 159, 137, 151, 166, 153, 144, 182, 133, 208, 172, 132, 185, 138, 164, 152, 149, 159, 149, 134, 148, 177, 167, 153, 221, 189, 121, 179, 192, 130, 142, 186, 191, 133, 147, 152, 172, 226, 189, 138, 167, 185, 151, 176, 157, 123, 168, 165, 150, 183, 143, 144, 196, 161, 172, 167, 117, 123, 152, 178, 136, 184, 179, 143, 154, 152, 152, 144, 205, 135, 162, 209, 151, 205, 131, 128, 157, 274, 154, 143, 137, 145, 179]
# A Traj [193, 221, 298, 213, 226, 322, 358, 188, 220, 285, 507, 214, 261, 289, 202, 224, 170, 345, 246, 211, 225, 259, 229, 208, 290, 197, 371, 270, 185, 286, 207, 249, 250, 215, 255, 233, 197, 231, 272, 233, 224, 398, 281, 151, 276, 291, 182, 221, 290, 332, 183, 243, 218, 244, 369, 332, 205, 250, 312, 237, 283, 249, 164, 242, 241, 240, 268, 226, 208, 328, 243, 240, 250, 161, 172, 231, 286, 185, 300, 281, 199, 241, 218, 236, 200, 364, 202, 257, 337, 226, 312, 182, 161, 257, 444, 224, 220, 199, 222, 282]
# A Time [0.027050021999457385, 0.037320210998586845, 0.04006789000413846, 0.05457702500279993, 0.03917543699935777, 0.05667156299750786, 0.06858573600038653, 0.03330866699980106, 0.03735478599992348, 0.04536983300204156, 0.09973413599800551, 0.037971752004523296, 0.04541141500521917, 0.04615031999856001, 0.038665587999275886, 0.029722539999056607, 0.055161980002594646, 0.10250408999854699, 0.04878109900164418, 0.03338833300222177, 0.033451626004534774, 0.05685451199678937, 0.040931408999313135, 0.035970363998785615, 0.054668907992891036, 0.034738030000880826, 0.07270171900017885, 0.05825601900141919, 0.027550251994398423, 0.09002513399900636, 0.038188949998584576, 0.04506101599690737, 0.04706571000133408, 0.03873533200385282, 0.0419156429998111, 0.051717695001570974, 0.039843225000367966, 0.03872554599365685, 0.04734459600149421, 0.029870957994717173, 0.05080988300323952, 0.07234241100377403, 0.03902049600583268, 0.03639800800010562, 0.04064132200437598, 0.0458469170043827, 0.03760941300424747, 0.04801510800461983, 0.042511943996942136, 0.04919732800044585, 0.030854970995278563, 0.04782653299480444, 0.030662123004731257, 0.044858114000817295, 0.05846513400319964, 0.06314379000104964, 0.028965353994863108, 0.06476067900075577, 0.06963493300281698, 0.03742511099699186, 0.0520925390010234, 0.043396465000114404, 0.027106553003250156, 0.03631799499999033, 0.03585076400486287, 0.03836904200579738, 0.03919798199785873, 0.038534988001629245, 0.031646209994505625, 0.052061110000067856, 0.04425075100152753, 0.03308086000470212, 0.042430501998751424, 0.03093948400055524, 0.028182520996779203, 0.04519180500210496, 0.03967958599969279, 0.02630404099909356, 0.050833007000619546, 0.04738311000255635, 0.02795725299802143, 0.05465244600054575, 0.03612060900195502, 0.04636515399761265, 0.026405684002384078, 0.06526302400015993, 0.04315802700148197, 0.0390529959986452, 0.05565513300098246, 0.03525301700574346, 0.06316653699468588, 0.03154392199940048, 0.0208376730006421, 0.04717710699333111, 0.07901727299758932, 0.036467961996095255, 0.03840744400076801, 0.03064909800013993, 0.04653409200545866, 0.04447939099918585]
# starting iteration 9
from random import choice
from timeit import default_timer as timer
import tensorflow as tf
import numpy as np
MAZE_DIM = 50
# ANALYSIS
def run_nn_agent(complete_grid, model):
original_source = (0,0) # we need to remember the original_source node bc in repeated A* source changes
source = original_source
n, m = (len(complete_grid), len(complete_grid[0]))
goal = (n-1, m-1) # dimensions of grid
num_actions = trajectory = 0
complete_grid = np.array(complete_grid)
grid = np.array([[0.5]*(m) for _ in range(n)])
grid[source[0]][source[1]] = 0.0
path = [source]
grid_states = []
movement_grid = [[-1]*m for _ in range(n)]
while source != goal: # execution step
valid_actions = []
# Generate input data for agent - grid, source + neighbors tuple
source_neighbors_data = [source[0], source[1]]
for dx, dy in [(0, 1), (-1, 0), (0, -1), (1, 0)]: # generate children
new_x, new_y = (source[0] + dx, source[1] + dy)
if 0 <= new_x < len(grid) and 0 <= new_y < len(grid):
source_neighbors_data.extend([new_x, new_y, grid[new_x][new_y]])
if grid[new_x][new_y] != 1:
valid_actions.append((dx, dy))
else:
source_neighbors_data.extend([0,0,1])
input_data = (np.array([grid]), np.array([source_neighbors_data]))
# Give agent grid and (try to) take step by agent
out = model(input_data).numpy()
prediction = np.argmax( out , axis = 1 )[0]
# Move to predicted state, if possible
num_actions += 1
action_movement_dict = {
0 : (-1,0), # North
1 : (0, 1), # East
2 : (1, 0), # South
3 : (0, -1) # West
}
# If attempted action is not valid for some reason, pick a random one that is
# Also, if we're attempting to go the same way we already have (in a loop), run a_star to get out of the loop
action = action_movement_dict[prediction]
if action not in valid_actions:
action = choice(valid_actions)
elif movement_grid[source[0]][source[1]] == prediction:
valid_actions.remove(action)
a_star_path, *_ = a_star(deepcopy(grid), source, goal)
idx = 1
next = a_star_path[0]
# Follow a_star until we hit a block or we are no longer in the loop
while source in path:
if idx >= len(a_star_path):
# print("Finished here")
break
curr = a_star_path[idx]
x, y = curr
grid[x][y] = complete_grid[x][y]
if grid[x][y] == 1: # If we hit a new block, break
break
# Move from source to curr
trajectory += 1
source = (x, y)
path.append(source)
idx += 1
continue
movement_grid[source[0]][source[1]] = prediction
# Move to new cell
x, y = (source[0] + action[0], source[1] + action[1])
grid[x][y] = complete_grid[x][y]
if grid[x][y] == 1:
continue
trajectory += 1
source = (x, y)
path.append(source)
# print(f"current path: {path[-10:]}")
return path, trajectory, num_actions
NUM_ITERATIONS = 30
NUM_TRIALS = 100
MAZE_DIM = 50
DENSITY = 0.3
SOLVABILITY = True
IS_TERRAIN_ENABLED = False
source = (0,0)
target = goal = (MAZE_DIM-1, MAZE_DIM-1)
p1_dense_model = tf.keras.models.load_model('./test_data_dense_p1/')
p1_cnn_model = tf.keras.models.load_model('./test_data_cnn_p1/')
def run_iteration(i=0):
# nn_path_lens = []
# nn_trajs = []
# nn_times = []
# cnn_path_lens = []
# cnn_trajs = []
# cnn_times = []
# a_path_lens = []
# a_trajs = []
# a_times = []
nn_path_sum = nn_traj_sum = nn_time_sum = 0
cnn_path_sum = cnn_traj_sum = cnn_time_sum = 0
a_path_sum = a_traj_sum = a_time_sum = 0
print(f"starting iteration {i}")
for j in range(NUM_TRIALS):
# print(f"trial {(i, j)}")
grid, _, _ = gen_gridworld(MAZE_DIM, DENSITY, SOLVABILITY, IS_TERRAIN_ENABLED, source, target)
start = timer()
path, traj, actions = run_nn_agent(grid, p1_dense_model)
stop = timer()
nn_time = stop - start
# nn_path_lens.append(len(set(path)))
# nn_trajs.append(traj)
# nn_times.append(nn_time)
nn_path_sum += len(set(path))
nn_traj_sum += traj
nn_time_sum += nn_time
start = timer()
path, traj, actions = run_nn_agent(grid, p1_cnn_model)
stop = timer()
cnn_time = stop - start
# cnn_path_lens.append(len(set(path)))
# cnn_trajs.append(traj)
# cnn_times.append(cnn_time)
cnn_path_sum += len(set(path))
cnn_traj_sum += traj
cnn_time_sum += cnn_time
start = timer()
_, repeated_a_star_path_set, _, _, _, _, traj, _, _ = repeated_a_star(grid)
stop = timer()
a_star_time = stop - start
# a_path_lens.append(len(repeated_a_star_path_set))
# a_trajs.append(traj)
# a_times.append(a_star_time)
a_path_sum += len(repeated_a_star_path_set)
a_traj_sum += traj
a_time_sum += a_star_time
return ((nn_path_sum / NUM_TRIALS, nn_traj_sum / NUM_TRIALS, nn_time_sum / NUM_TRIALS), (cnn_path_sum / NUM_TRIALS, cnn_traj_sum / NUM_TRIALS, cnn_time_sum / NUM_TRIALS), (a_path_sum / NUM_TRIALS, a_traj_sum / NUM_TRIALS, a_time_sum / NUM_TRIALS))
# print("NN Path", nn_path_lens)
# print("NN Traj", nn_trajs)
# print("NN Time", nn_times)
# print("CNN Path", cnn_path_lens)
# print("CNN Traj", cnn_trajs)
# print("CNN Time", cnn_times)
# print("A Path", a_path_lens)
# print("A Traj", a_trajs)
# print("A Time", a_times)
nn_path_avgs = []
nn_traj_avgs = []
nn_time_avgs = []
cnn_path_avgs = []
cnn_traj_avgs = []
cnn_time_avgs = []
a_path_avgs = []
a_traj_avgs = []
a_time_avgs = []
for i in range(NUM_ITERATIONS):
rets = run_iteration(i)
nn_path_avgs.append(rets[0][0])
nn_traj_avgs.append(rets[0][1])
nn_time_avgs.append(rets[0][2])
cnn_path_avgs.append(rets[1][0])
cnn_traj_avgs.append(rets[1][1])
cnn_time_avgs.append(rets[1][2])
a_path_avgs.append(rets[2][0])
a_traj_avgs.append(rets[2][1])
a_time_avgs.append(rets[2][2])
print(nn_path_avgs)
print(nn_traj_avgs)
print(nn_time_avgs)
print(cnn_path_avgs)
print(cnn_traj_avgs)
print(cnn_time_avgs)
print(a_path_avgs)
print(a_traj_avgs)
print(a_time_avgs)
starting iteration 0
starting iteration 1
starting iteration 2
starting iteration 3
starting iteration 4
starting iteration 5
starting iteration 6
starting iteration 7
starting iteration 8
starting iteration 9
[177.4, 157.6, 175.6, 156.2, 189.4, 188.0, 162.4, 164.8, 151.8, 184.8]
[222.0, 180.8, 217.6, 184.8, 251.6, 245.6, 205.6, 192.4, 176.8, 226.0]
[1.934705733403098, 1.645674962400517, 1.8060789418013883, 1.6095205746009014, 2.0631797833993915, 2.0514500179997412, 1.8833281233994057, 1.617990298998484, 1.6302852630004054, 2.040595890399709]
[226.0, 176.2, 157.0, 142.6, 158.8, 175.4, 172.0, 161.0, 158.2, 215.4]
[298.4, 206.8, 180.4, 157.2, 184.8, 207.2, 205.2, 184.8, 188.8, 267.2]
[4.266449646399996, 3.369963846601604, 2.9987810671998885, 2.577833686601662, 3.0480733748001514, 3.3660197225995945, 3.5337548686002265, 3.025409155401576, 3.049412638200738, 4.3841205936012555]
[130.2, 132.2, 127.4, 123.0, 123.4, 133.4, 125.8, 127.0, 120.6, 139.8]
[258.2, 251.2, 230.6, 206.4, 250.0, 263.0, 269.6, 234.0, 231.2, 287.4]
[0.04768102299858583, 0.04122461159859085, 0.03712173440144397, 0.03736739440064411, 0.04637555099907331, 0.0517265602000407, 0.0513785564005957, 0.034284745600598396, 0.0453821864008205, 0.056263956600741946]
nn_path_avgs = [177.4, 157.6, 175.6, 156.2, 189.4, 188.0, 162.4, 164.8, 151.8, 184.8]
cnn_path_avgs = [226.0, 176.2, 157.0, 142.6, 158.8, 175.4, 172.0, 161.0, 158.2, 215.4]
a_path_avgs = [130.2, 132.2, 127.4, 123.0, 123.4, 133.4, 125.8, 127.0, 120.6, 139.8]
nn_traj_avgs = [222.0, 180.8, 217.6, 184.8, 251.6, 245.6, 205.6, 192.4, 176.8, 226.0]
cnn_traj_avgs = [298.4, 206.8, 180.4, 157.2, 184.8, 207.2, 205.2, 184.8, 188.8, 267.2]
a_traj_avgs = [258.2, 251.2, 230.6, 206.4, 250.0, 263.0, 269.6, 234.0, 231.2, 287.4]
nn_time_avgs = [1.934705733403098, 1.645674962400517, 1.8060789418013883, 1.6095205746009014, 2.0631797833993915, 2.0514500179997412, 1.8833281233994057, 1.617990298998484, 1.6302852630004054, 2.040595890399709]
cnn_time_avgs = [4.266449646399996, 3.369963846601604, 2.9987810671998885, 2.577833686601662, 3.0480733748001514, 3.3660197225995945, 3.5337548686002265, 3.025409155401576, 3.049412638200738, 4.3841205936012555]
a_time_avgs = [0.04768102299858583, 0.04122461159859085, 0.03712173440144397, 0.03736739440064411, 0.04637555099907331, 0.0517265602000407, 0.0513785564005957, 0.034284745600598396, 0.0453821864008205, 0.056263956600741946]
import matplotlib.pyplot as plt
its = [0,1,2,3,4,5,6,7,8,9]
plt.plot(its, nn_path_avgs)
plt.plot(its, cnn_path_avgs)
plt.plot(its, a_path_avgs)
plt.legend(["NN", "CNN", "A Star"])
plt.xlabel("Iterations")
plt.ylabel("Average Path")
plt.title("Average Path For Each Agent")
plt.grid()
plt.show()
its = [0,1,2,3,4,5,6,7,8,9]
plt.plot(its, nn_traj_avgs)
plt.plot(its, cnn_traj_avgs)
plt.plot(its, a_traj_avgs)
plt.legend(["NN", "CNN", "A Star"])
plt.xlabel("Iterations")
plt.ylabel("Average Trajectory")
plt.title("Average Trajectory For Each Agent")
plt.grid()
plt.show()
its = [0,1,2,3,4,5,6,7,8,9]
plt.plot(its, nn_time_avgs)
plt.plot(its, cnn_time_avgs)
plt.plot(its, a_time_avgs)
plt.legend(["NN", "CNN", "A Star"])
plt.xlabel("Iterations")
plt.ylabel("Average Times")
plt.title("Average Times For Each Agent")
plt.grid()
plt.show()
class CellState(Enum):
BLOCKED = 1
OPEN = 2
UNKNOWN = 3
def __int__(self):
return self.value
class Intel:
def __init__(self, N: int = 0):
self.N = N
self.visited = False
self.status = CellState.UNKNOWN
self.C = -1 # Value of -1 means we don't know this value (cell hasn't been visited yet)
self.B = 0
self.E = 0
self.H = N
# self.blocked_probability = 0.5 # Used for agent 5
def __str__(self):
return f"N: {self.N}, visited: {self.visited}, status: {self.status}, C: {self.C}, B: {self.B}, " \
f"E: {self.E}, H: {self.H}"
def __repr__(self):
return self.__str__()
@staticmethod
def convert(self):
return np.array([self.N, int(self.visited), int(self.status), self.C, self.B, self.E, self.H]) # didn't add blocked_probability bc we only care about agent 3
INTEL_SIZE = len(Intel().__dict__.keys())
def _run_single_inference_agent3(grid_len: int, kb: List[List[Intel]]) -> bool:
changed = False
for row_idx, row in enumerate(kb):
for col_idx, intel in enumerate(row):
# If the cell has not been visited yet, we can't infer anything about its neighbors
# since C has not been set
# If Hx=0: nothing remains to be inferred about cell x.
if not intel.visited or intel.H == 0:
continue
# If Cx=Bx: all remaining hidden neighbors of x are empty.
if intel.C == intel.B:
for (n_row, n_col) in _get_neighbor_indices(grid_len, row_idx, col_idx):
if kb[n_row][n_col].status == CellState.UNKNOWN:
update_cell_status(CellState.OPEN, kb, n_row, n_col, grid_len)
changed = True
# If Nx−Cx=Ex: all remaining hidden neighbors of x are blocked.
elif intel.N - intel.C == intel.E:
for (n_row, n_col) in _get_neighbor_indices(grid_len, row_idx, col_idx):
if kb[n_row][n_col].status == CellState.UNKNOWN:
update_cell_status(CellState.BLOCKED, kb, n_row, n_col, grid_len)
changed = True
return changed
def agent3(grid: List[List[int]], dataformat:int = None, inference: Callable = _run_single_inference_agent3):
"""
Args:
Returns:
list(tuple(int, int)): the path from the source node to the goal node which consists of coordinate points (i.e. x,y) in the form of tuples.
int: the length of the discovered path from source to goal (i.e. the length of the first return value)
list(tuple(int,int)): all visited squares from the source point. Contains int tuples in the form of (x,y).
int: trajectory - num of moves the agent makes
int: num of times the agent assumes wrong and enters a blocked square
"""
knowledge_base = create_initial_kb(grid)
grid_len = len(grid)
n, m = grid_len-1, grid_len-1
source, goal = (0, 0), (n,m)
full_path = [source]
full_path_and_blocks = []
solvable = True
num_bumps = 0
# seen = defaultdict(int)
grid_states = []
grid_sources = []
actions = []
while solvable and goal != full_path[-1]:
# Create current grid based on current knowledge base
current_grid = np.zeros((len(grid), len(grid[0])))
for row_idx, row in enumerate(grid):
for col_idx, _ in enumerate(row):
blocked = 1 if knowledge_base[row_idx][col_idx].status == CellState.BLOCKED else 0
current_grid[row_idx][col_idx] = blocked
path, _, _, _, _ = a_star(current_grid, source, goal)
if path == []: solvable = False
for path_idx, (row_idx, col_idx) in enumerate(path):
ran_inference = False
if not knowledge_base[row_idx][col_idx].visited:
if grid[row_idx][col_idx] == 0:
knowledge_base[row_idx][col_idx].C = \
_get_num_blocked_neighbors(grid, row_idx, col_idx)
knowledge_base[row_idx][col_idx].visited = True
if knowledge_base[row_idx][col_idx].status == CellState.UNKNOWN:
cell_state = \
CellState.BLOCKED if grid[row_idx][col_idx] == 1 else CellState.OPEN
update_cell_status(cell_state, knowledge_base, row_idx, col_idx, grid_len)
run_inference(grid_len, knowledge_base, inference)
ran_inference = True
full_path_and_blocks.append((row_idx, col_idx))
# If we hit a block, restart and try again. Otherwise, continue
if grid[row_idx][col_idx] == 1 and full_path != []:
num_bumps += 1
source = full_path[-1]
break
# Only add to the path if we aren't at the source
# (otherwise we would have duplicates on restarts)
if (row_idx, col_idx) != source:
if dataformat:
if dataformat == 1:
source_and_neighbors = []
for i, (dx, dy) in enumerate([(0,0), (0,1), (1,0), (0,-1), (-1,0), (1,1), (-1,-1), (1,-1), (-1,1)]):
new_x, new_y = [row_idx + dx, col_idx + dy]
if 0 <= new_x < n and 0 <= new_y < m:
source_and_neighbors.extend([new_x, new_y, *(Intel.convert(knowledge_base[new_x][new_y]))])
else:
source_and_neighbors.extend([new_x, new_y, *([0] * INTEL_SIZE)])
grid_states.append(source_and_neighbors)
elif dataformat == 2:
grid_state = np.empty(shape=(len(grid), len(grid[0]), INTEL_SIZE))
for i in range(len(grid)):
for j in range(len(grid[0])):
for k,v in enumerate(Intel.convert(knowledge_base[i][j])):
grid_state[i][j][k] = v
grid_states.append(grid_state)
grid_sources.append((source))
actions.append(int(_get_cardinal_direction(*source, row_idx, col_idx)))
full_path.append((row_idx, col_idx))
# If there is a block in our current path, but the current cell was not blocked
if ran_inference and any(knowledge_base[x][y].status == CellState.BLOCKED for (x,y) in path[path_idx+1:]):
source = full_path[-1]
break
if solvable:
# Create current grid based on current knowledge base
current_grid = np.zeros((len(grid), len(grid[0])))
for row_idx, row in enumerate(grid):
for col_idx, _ in enumerate