Курсовая работа «Применение библиотек машинного обучения в интеллектуальном анализе» по дисциплине: «Технологии баз данных»

Приложения
Код программы Модель персептрон Розенблатта:
import matplotlib.pyplot as plt
import numpy as np
import random
import math
class SemicircleGenerator(object):
"""docstring for SemicircleGenerator"""

def __init__(self, StartLocation, RadiusList, Orientation):

super(SemicircleGenerator, self).__init__()
self.StartLocation = StartLocation
self.MaxRadius = max(RadiusList)
self.MinRadius = min(RadiusList)
self.Orientation = Orientation

def Gen_SemicircleData(self, BatchSize):

for _ in range(BatchSize):
Radius = random.uniform(self.MinRadius, self.MaxRadius)
BiasX = random.uniform(- Radius, Radius)
BiasY = math.sqrt(Radius * Radius - BiasX * BiasX)
if self.Orientation == "+":
yield [BiasX + self.StartLocation[0], BiasY
+ self.StartLocation[1]]
else:
yield [self.StartLocation[0] - BiasX,
self.StartLocation[1] - BiasY]
class Rosenblatt(object):
"""docstring for Rosenblatt"""

def __init__(self, InputNum):

super(Rosenblatt, self).__init__()
self.Weight = np.zeros([InputNum + 1, 1])
self.TrainRaito = 1

def ActivitionFunction(self, InputData):

# print(InputData, np.zeros(InputData.shape))
# print((InputData > np.zeros(InputData.shape)).all())
if (InputData > np.zeros(InputData.shape)).all() == True:
# print("1")
return 1
else:
# print("-1")
return -1

def Feedforward(self, InputData):

return self.ActivitionFunction(
np.matmul(self.Weight.T, np.hstack((np.ones([1, 1]), InputData)).T))

def TrainOneStep(self, InputData, RightResult):

Result = self.Feedforward(InputData)
if Result != RightResult:
self.ChangeWeight(InputData, RightResult, Result)
def ChangeWeight(self, InputData, RightResult, Result):
# print(self.TrainRaito *
# (RightResult - Result))
self.Weight += self.TrainRaito * \
(RightResult - Result) * np.hstack((np.ones([1, 1]), InputData)).T

def SetTrainRatio(self, Ratio):

self.TrainRaito = Ratio
neural = Rosenblatt(2)
dataset1 = SemicircleGenerator([0, 0], [4, 6], "+")
dataset2 = SemicircleGenerator([7, -2], [4, 6], "-")
testdata_x, testdata_y = [], []

for data in dataset1.Gen_SemicircleData(1000):

neural.TrainOneStep(np.array([data]), 1)
testdata_x.append(data[0])
testdata_y.append(data[1])
for data in dataset2.Gen_SemicircleData(1000):
neural.TrainOneStep(np.array([data]), -1)
testdata_x.append(data[0])
testdata_y.append(data[1])

print(neural.Weight)

x_data, y_data = [-6, 13], []
print(neural.Weight[1][0])
for i in x_data:
y_data.append(- (neural.Weight[0][0] + i *
neural.Weight[1][0]) / (neural.Weight[2][0]))

plt.plot(x_data, y_data, color="red")

plt.scatter(testdata_x, testdata_y)
plt.show()