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import time
from collections import defaultdict
import pickle
from functools import reduce
def flatten(l):
return reduce(lambda a,b:a+b, l)
def quantile(values, q):
if not isinstance(values, list):
return quantile(flatten(map(lambda x : [x[0]]*x[1], sorted(values.items(),key=lambda x:x[0]))), q)
else:
return values[ int(len(values)*q) ]
def avg(values):
if isinstance(values, list):
return sum(values)/len(values)
else:
return int(sum(map( lambda x : x[0]*x[1], values.items() )) / sum(map(lambda x : x[1], values.items())))
def stddev(values):
a=avg(values)
return avg(list(map(lambda v : (v-a)**2, values)))
def normalize(values):
a=avg(values)
return [x/a for x in values]
class StatData():
pass
def return_empty_list():
return []
def return_zero():
return 0
def return_defaultdict_with_zeros():
return defaultdict(return_zero)
class Stats:
def __init__(self,c,data=None):
self.c = c
if data == None:
self.data = StatData()
self.data.version = 1
self.data.min_mass = 0
self.data.max_mass = 0
self.data.current_mass = 0
self.data.mass_history = []
self.data.pos_history = []
self.data.cell_aggressivity = {}
self.data.cell_split_frequency = {}
self.data.cell_defensiveness = {}
self.data.size_vs_speed = defaultdict(return_defaultdict_with_zeros)
self.data.size_vs_visible_window = defaultdict(return_empty_list)
self.data.mass_vs_visible_window = defaultdict(return_empty_list)
else:
self.data = data
def log_mass(self, mass):
self.data.mass_history.append((time.time(), mass))
self.data.current_mass = mass
if mass > self.data.max_mass:
self.data.max_mass = mass
if mass < self.data.min_mass:
self.data.min_mass = mass
def log_pos(self, pos):
self.data.pos_history.append((time.time(), (pos[0], pos[1])))
def update_cell_aggressivity(self, cell, value):
self.data.cell_aggressivity[cell] = value
def update_cell_split_frequency(self, cell, value):
self.data.cell_split_frequency[cell] = value
def update_cell_defensiveness(self, cell, value):
self.data.cell_defensiveness[cell] = value
def get_last_steps(self, list, steps = 10):
return list[-steps:]
def process_frame(self):
self.log_pos(self.c.player.center)
self.log_mass(self.c.player.total_mass)
cells = self.c.world.cells.values()
own_cells = self.c.player.own_cells
own_total_size = sum( map(lambda cell : cell.size, own_cells) )
own_total_mass = sum( map(lambda cell : cell.mass, own_cells) )
n = 3
for cell in filter(lambda cell : not cell.is_food and not cell.is_virus and not cell.is_ejected_mass, cells):
if hasattr(cell,'poslog') and len(cell.poslog) > n+1:
cellspeed = 0
for i in range(1,n+1):
cellspeed += (cell.poslog[-i] - cell.poslog[-i-1]).len() / n
cellspeed = int(cellspeed*10)/10
self.data.size_vs_speed[cell.size][cellspeed] += 1
visible_width = max( map(lambda cell : cell.pos.x - cell.size, cells) ) - min( map(lambda cell : cell.pos.x + cell.size, cells) )
visible_height = max( map(lambda cell : cell.pos.y - cell.size, cells) ) - min( map(lambda cell : cell.pos.y + cell.size, cells) )
self.data.size_vs_visible_window[own_total_size].append((visible_width,visible_height))
self.data.mass_vs_visible_window[own_total_mass].append((visible_width,visible_height))
def save(self,filename):
pickle.dump(self.data, open(filename,"wb"))
def load(filename):
return Stats(None, pickle.load(open(filename,"rb")))
def merge(self, filename):
data2 = pickle.load(open(filename,"rb"))
self.data.min_mass = min(self.data.min_mass, data2.min_mass)
self.data.max_mass = max(self.data.max_mass, data2.max_mass)
for i in data2.size_vs_visible_window:
self.data.size_vs_visible_window[i] += data2.size_vs_visible_window[i]
for i in data2.mass_vs_visible_window:
self.data.mass_vs_visible_window[i] += data2.mass_vs_visible_window[i]
for i in data2.size_vs_speed:
for j in data2.size_vs_speed[i]:
self.data.size_vs_speed[i][j] += data2.size_vs_speed[i][j]
def analyze_speed(self):
results=[]
for size, values in sorted(self.data.size_vs_speed.items(), key=lambda x : x[0]):
minimum = quantile(values, 0.2)
average = quantile(values, 0.5)
maximum = quantile(values, 0.8)
results += [(size,maximum,average,minimum,False,False,False,sum(values.values()))]
# mark outliers
for i in range(1, len(results)-1):
for j in range(1,4):
if abs(results[i][j] - results[i-1][j]) > 2 and abs(results[i][j] - results[i+1][j]) > 2:
tmp = list(results[i])
tmp[j+3] = True
results[i] = tuple(tmp)
coeff_vs_stddev = []
for coeff in [x/100 for x in range(10,100,1)]:
products = []
for size, maximum, average, minimum, maxoutlier, avgoutlier, minoutlier, ndata in results:
if not maxoutlier:
products += [size**coeff * maximum]
coeff_vs_stddev += [(coeff, avg(products), stddev(normalize(products)))]
best = min(coeff_vs_stddev, key=lambda v:v[2])
print("size\tcalc\tmax\tavg\tmin\t\tndata")
for size, maximum, average, minimum, maxoutlier, avgoutlier, minoutlier, ndata in results:
print(str(size) + ":\t" + "%.1f" % (best[1] / size**best[0]) + "\t" + ("*" if maxoutlier else "") + str(maximum) + "\t" + ("*" if avgoutlier else "") + str(average) + "\t" + ("*" if minoutlier else "") + str(minimum) + "\t\t" + str(ndata))
print("size**"+str(best[0])+" * speed = "+str(best[1]) )
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