def main():
#import the data from a csv
car_data = np.genfromtxt('car_data.csv', delimiter=',')
#call the tree creator module and pass the name of the json file to it
dTree = DecisionTree('jsonTrees/' + testName + '.json')
scores = []
#track the best score and data
best = [0, 0]
test = []
for data in car_data:
#change the inputs for each of the cars in the tree
dTree.changeInputs(convertCarData(data))
#get the score for that car
score = dTree.run()
#check if its the highest
if (score > best[0]):
best[0] = score
best[1] = data
#add it to the list of scores
scores.append(score)
test.append([score, data.tolist()])
#print("best",best[0],best[1])
test = sorted(test, key=lambda x: x[0], reverse=True)
pprint(test[0:3])
#create a normilized histagram of the scores
n, bins, patches = plt.hist(scores,
normed=1,
facecolor='green',
alpha=0.75)
plt.title(testName + wValue)
#save the image to a file
plt.savefig("graphs/" + testName + wValue + ".png", bbox_inches='tight')
#show the image
plt.show()
def main():
fmemFile = File("fmemFile.csv")
#import the data from a csv
car_data = np.genfromtxt('car_data.csv', delimiter=',')
#call the tree creator module and pass the name of the json file to it
aTree = DecisionTree('jsonTrees/' + testA + '.json')
nTree = DecisionTree('jsonTrees/' + testN + '.json')
eTree = DecisionTree('jsonTrees/' + testE + '.json')
#iterator = car_data[np.random.randint(car_data.shape[0], size=100), :]
#iterator = car_data
iterator = inputs2
for inputs in iterator:
#change the inputs for each of the cars in the tree
#inputs = convertCarData(inputs)
aTree.changeInputs(inputs)
nTree.changeInputs(inputs)
eTree.changeInputs(inputs)
#get the score for that car
aScore = aTree.run()
nScore = nTree.run()
eScore = eTree.run()
print("Inputs:", inputs)
print("ASCORE #######:", aScore)
print("NSCORE #######:", nScore)
print("ESCORE #######:", eScore)
eScore = np.array(eScore)
f1 = MemFunc('trap', aScore)
X = np.arange(0, 1, .05)
l1, = plt.plot(X, [f1.memFunc(i) for i in X],
c='r',
linewidth=2.0,
label="AlphaCuts")
l2, = plt.plot(eScore[:, 0],
eScore[:, 1],
c='b',
linewidth=2.0,
label="Extention Principle")
l3 = plt.axvline(nScore, c='g', linewidth=2.0, label="Crisp")
plt.legend(handles=[l1, l2, l3])
plt.title("Regular Title")
plt.xlabel("Output Score")
plt.ylabel("Membership Value")
#Batch Save Rember to remove input
#plt.savefig("test.png")
plt.show()
break