def testBench(request) :
grab_list = []
try :
print util.parse("apphello", "3.2.1", "0")
grab_list = util.parse("apphello", "3.2.1", "0")
grab_list = ["foo", "bar", "baz"]
except :
return HttpResponse("No lines found")
lineString = ""
for line in grab_list :
lineString = lineString + line + "<br>"
#t = loader.get_template('/home/kawaii5/Stationerry/StationerryWeb/StationerryWebApp/templates/stationerry/testbench.html')
return HttpResponse(lineString)
def __init__(self, f):
super().__init__(f)
f = parse(f)
self.type = f.get("type")
self.damage = int(f.get("damage"))
def __init__(self, f):
super().__init__(f)
f = parse(f)
self.attacks = {}
for i in f.get("attacks").split("\n"):
att = attack(i)
self.attacks[att.getName()] = att
def __init__(self, f):
super().__init__(f)
f = parse(f)
self.protection = {}
for i in f.get("protection").split("\n"):
i = i.split(" ")
self.protection[i[0]] = int(i[1])
self.type = f.get("type")
def __init__(self, f):
f = parse(f)
self.name = f.get("name")
self.desc = f.get("desc")
self.health = int(f.get("health"))
self.weapon = weapon(f.get("weapon"))
self.armor = armorSet()
for i in ["helm", "chest", "legs", "boots"]:
if f.get(i, -1) != -1:
armor.set(armorPiece(f.get(i)))
#How can drops be given to the player? It's a wierd thing for attack to handle
#maps a tuple of the range to an item, eg (0,15):sword.
#usage: get a random number from 0 to 100, if 0 <= x <= 15 drop = sword
self.drops = {}
for i in f.get("drops").split("\n"):
i = i.split(" ")
dropRange = i[1].split("-")
self.drops[(dropRange[0], dropRange[1])] = createItem(i[0])
def createItem(f):
a = {"item": item, "armorPiece": armorPiece, "weapon": weapon}
b = parse(f)
return a[b.get("class")](f)
def __init__(self, f):
f = parse(f)
protection = f.get("protection")
self.name = f.get("name")
self.value = f.get("value")
import collections
import matplotlib.pyplot as plt
import data_visualizer as dv
import utilities as ut
features, labels = ut.parse(data_limit=-1)
test_features = ut.parse(path_feature='test_data.csv',
path_labels='',
data_limit=-1)
#print 'featur number ' +str(len(test_features))
X_test, Y_test, X_train, Y_train = ut.divide_set(features, labels)
#print len(X_test), len(Y_test), len(Y_train)
#print "features \n", features[1:10]
#print "labels \n", labels[1:10]
"""showing the distribution of classes"""
"""counter = collections.Counter(labels)
print(counter.values())
print(counter.keys())
print(counter.most_common(3))
width = 1 / 1.5
plt.bar(counter.keys(), counter.values(), width, color="blue")
plt.show()""" ""
"""classification"""
#Y_pred = ut.do_nn(X_test, Y_test, X_train, Y_train)
#print ut.check(Y_test, Y_pred)
#dv.confusionMatrix(Y_test, Y_pred)
"""logloss classification"""
import utilities as lk from sklearn import neighbors, datasets #from sklearn.neighbors import NearestNeighbors import numpy as np import data_visualizer as dv from sklearn.metrics import accuracy_score n_neighbors = 31 h = .02 X, Y = lk.parse() X_test, Y_test, X_train, Y_train = lk.divide_set(X, Y) dv.visualizeLabels(Y) clf = neighbors.KNeighborsClassifier(n_neighbors) print 'fitting nearest neighbors' clf.fit(X_train, Y_train) print 'predicting' Y_pred = clf.predict(X_test) print(accuracy_score(Y_test, Y_pred)) dv.confusionMatrix(Y_test, Y_pred, True)