shuffle(irises) # Get our lines of data in random order
for iris in irises:
parameters = [float(n) for n in iris[:4]]
iris_parameters.append(parameters)
species = iris[4]
if species == 'Iris-setosa':
iris_classifications.append([1.0, 0.0, 0.0])
elif species == 'Iris-versicolor':
iris_classifications.append([0.0, 1.0, 0.0])
else:
iris_classifications.append([0.0, 0.0, 1.0])
iris_species.append(species)
normalize_by_feature_scaling(iris_parameters)
iris_network = Network([4, 6, 3], 0.3)
def iris_interpret_output(output) -> str:
if max(output) == output[0]:
return 'Iris-setosa'
elif max(output) == output[1]:
return 'Iris-versicolor'
else:
return 'Iris-virginica'
# Train over the first 140 irises in the data set 50 times
iris_trainers = iris_parameters[:140]
iris_trainers_corrects = iris_classifications[:140]
for _ in range(50):
with open(os.path.join(BASE_DIR, 'wine.csv'), mode='r') as wine_file:
# quoting参数的意思是不要把数字引起来
wines: List = list(csv.reader(wine_file, quoting=csv.QUOTE_NONNUMERIC))
shuffle(wines)
for wine in wines:
parameters: List[float] = [float(n) for n in wine[1:14]]
wine_parameters.append(parameters)
species: int = int(wine[0])
if species == 1:
wine_classifications.append([1.0, 0.0, 0.0])
elif species == 2:
wine_classifications.append([0.0, 1.0, 0.0])
else:
wine_classifications.append([0.0, 0.0, 1.0])
wine_species.append(species)
normalize_by_feature_scaling(wine_parameters)
wine_network: Network = Network([13, 7, 3], 0.9)
def wine_interpret_output(output: List[float]) -> int:
if max(output) == output[0]:
return 1
elif max(output) == output[1]:
return 2
else:
return 3
# 用前150个数据训练10次
wine_trainers: List[List[float]] = wine_parameters[:150]
wine_trainers_corrects: List[List[float]] = wine_classifications[:150]
for _ in range(10):
