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84 lines
3.3 KiB
84 lines
3.3 KiB
import random
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import numpy as np
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import data as gl_data
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def linear_function(coefficient, x): # 构造一次函数y = a * X + b
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return coefficient[0] * x + coefficient[1]
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def quadratic_function(coefficient, x): # 构造二次函数y = a * X^2 + b * X + c
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return coefficient[0] * x ** 2 + coefficient[1] * x + coefficient[2]
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def qubic_function(coefficient, x): # 构造三次函数y = a* X^3 + b * X^2 + c * X + d
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return coefficient[0] * x ** 3 + coefficient[1] * x ** 2 + coefficient[2] * x + coefficient[3]
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def quartic_function(coefficient, x): # 构造四次函数
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return coefficient[0] * x ** 4 + coefficient[1] * x ** 3 + coefficient[2] * x ** 2 + coefficient[3] * x + \
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coefficient[4]
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# 编程4.1 -----------------------------------------
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def random_points(sampleNum, Low, High):
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sx, sy=[], []
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for i in range(sampleNum):
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sx.append(random.uniform(Low, High)) # 生成随机浮点数
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sy.append(random.uniform(Low, High))
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sx, sy = np.array(sx), np.array(sy) # 列表转数组
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gl_data.SampleData = np.array(list(zip(sx, sy))) # 拼接成二维数组并存入全局变量
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return gl_data.SampleData
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# if __name__ == '__main__':
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# sampleData = random_points(20, 0, 1000)
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# print(sampleData )
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# 编程4.1 END-----------------------------------------
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# 编程4.2 -----------------------------------------
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def compute_curveData(Low, High, step, coefficient, m): # coefficient代表二次函数系数
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x_values = np.arange(Low, High, step)
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if m == 1:
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y_values = linear_function(coefficient, x_values)
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if m == 2:
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y_values = quadratic_function(coefficient, x_values) # 调用quadratic_function(x)函数得到y值
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if m == 3:
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y_values = qubic_function(coefficient, x_values)
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if m == 4:
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y_values = quartic_function(coefficient, x_values)
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gl_data.CurveData = np.column_stack((x_values, y_values)) # 将x,y堆叠成二维数组
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return gl_data.CurveData
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# if __name__ == '__main__':
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# coefficient = [1, 2, 3]
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# curveData = compute_curveData(gl_data.LOW, gl_data.HIGH, 1, coefficient,2)
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# print(curveData)
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# 由于出现了数据稳定性的问题，因此在这里多了一个需要进行标准化处理的步骤
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def compute_curveData2(low, high, step, theta, m, x_mean, x_std):
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x_fit = np.arange(low, high, step)
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# 将x_fit标准化
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x_fit_normalized = (x_fit - x_mean) / x_std
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A_fit = np.vander(x_fit_normalized, m + 1, increasing=True)
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y_fit = A_fit @ theta
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gl_data.CurveData = np.column_stack((x_fit, y_fit))
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return gl_data.CurveData
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# 在X5中由于拓展了函数因此修改
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def compute_curveData_X5(func, popt):
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x_values = np.arange(gl_data.LOW, gl_data.HIGH, 1)
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y_values = func(x_values, *popt)
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gl_data.CurveData = np.column_stack((x_values, y_values))
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return gl_data.CurveData
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# 进行标准化的X5函数
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def compute_curveData_X5_2(func, popt, sampleData):
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sx, sy = sampleData[:, 0], sampleData[:, 1]
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x_values = np.arange(gl_data.LOW, gl_data.HIGH, 1)
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x_values_normalized = (x_values - np.mean(sx)) / np.std(sx)
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y_values = func(x_values_normalized, *popt)
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gl_data.CurveData = np.column_stack((x_values, y_values))
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return gl_data.CurveData
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# 编程4.2 END-----------------------------------------
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