그 지금 하려고 하는게 r2 값을 최대한 올려보려고 하고 있거든

지금 쓰는게 5차 함수 f(x,y) 꼴 다 쓰기는 힘들어서 그냥 저렇게만 넣었거든

저 상태로 계산하면 r2 이 0.95까지는 나옴

저거 최대한 1까지 올리는 방법 없을까?

1에 가까울수록 원래 데이터랑 값이 비슷하다거든

그냥 5차 함수 하나 하나 다 넣으면 값 비슷 하게 나오려나?

플롯은 이래 나옴

저 밑에 까지 대부분 다 커버 하게 하고 싶은데

수식 자체를 잘못 고른걸까?

import numpy, scipy, scipy.optimize

import matplotlib

from mpl_toolkits.mplot3d import  Axes3D

from matplotlib import cm # to colormap 3D surfaces from blue to red

import matplotlib.pyplot as plt

import numpy as np

graphWidth = 800 # units are pixels

graphHeight = 600 # units are pixels

import pandas as pd

df = pd.read_excel(r'C:\Users\JRKIM\Desktop\data\2513data.xlsx')

# 3D contour plot lines

numberOfContourLines = 16

xData = df.loc[:,'TRUE_x_0']

yData = df.loc[:,'TRUE_y_0']

zData = df.loc[:,'dr']

# place the data in a single list

data = [xData, yData, zData]

def SurfacePlot(func, data, fittedParameters):

    f = plt.figure(figsize=(graphWidth/100.0, graphHeight/100.0), dpi=100)

    matplotlib.pyplot.grid(True)

    axes = Axes3D(f)

    # extract data from the single list

    x_data = data[0]

    y_data = data[1]

    z_data = data[2]

    xModel = numpy.linspace(min(x_data), max(x_data))

    yModel = numpy.linspace(min(y_data), max(y_data))

    X, Y = numpy.meshgrid(xModel, yModel)

    Z = func(numpy.array([X, Y]), *fittedParameters)

    axes.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=1, antialiased=True, alpha = 0.3)

    axes.scatter(x_data, y_data, z_data) # show data along with plotted surface

    axes.set_title('Surface Plot (click-drag with mouse)') # add a title for surface plot

    axes.set_xlabel('X Data') # X axis data label

    axes.set_ylabel('Y Data') # Y axis data label

    axes.set_zlabel('Z Data') # Z axis data label

    plt.show()

def ContourPlot(func, data, fittedParameters):

    f = plt.figure(figsize=(graphWidth/100.0, graphHeight/100.0), dpi=100)

    axes = f.add_subplot(111)

    x_data = data[0]

    y_data = data[1]

    z_data = data[2]

    xModel = numpy.linspace(min(x_data), max(x_data), 20)

    yModel = numpy.linspace(min(y_data), max(y_data), 20)

    X, Y = numpy.meshgrid(xModel, yModel)

    Z = func(numpy.array([X, Y]), *fittedParameters)

    axes.plot(x_data, y_data, 'o')

    axes.set_title('Contour Plot') # add a title for contour plot

    axes.set_xlabel('X Data') # X axis data label

    axes.set_ylabel('Y Data') # Y axis data label

    CS = matplotlib.pyplot.contour(X, Y, Z, numberOfContourLines, colors='k')

    matplotlib.pyplot.clabel(CS, inline=1, fontsize=10) # labels for contours

    plt.show()

def ScatterPlot(data):

    f = plt.figure(figsize=(graphWidth/100.0, graphHeight/100.0), dpi=100)

    matplotlib.pyplot.grid(True)

    axes = Axes3D(f)

    # extract data from the single list

    x_data = data[0]

    y_data = data[1]

    z_data = data[2]

    axes.scatter(x_data, y_data, z_data)

    axes.set_title('Scatter Plot (click-drag with mouse)')

    axes.set_xlabel('X Data')

    axes.set_ylabel('Y Data')

    axes.set_zlabel('Z Data')

    plt.show()

def func(data, a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p):

    # extract data from the single list

    x1 = data[0]

    x2 = data[1]

    return a*data[0]**5+b*x1**4+c*x1**3+d*x1**2+e*x1+f*x2**5+g*x2**4+h*x2**3+i*x2**2+j*x2+k*(x1**5)*(x2**5)+l*(x1**4)*(x2**4)+m*(x1**3)*(x2**3)+n*(x1**2)*(x2**2)+o*(x1)*(x2)+p

if __name__ == "__main__":    

    initialParameters = np.random.rand(16)

    # here a non-linear surface fit is made with scipy's curve_fit()

    fittedParameters, pcov = scipy.optimize.curve_fit(func, [xData, yData], zData, p0 = initialParameters, maxfev=10000000)

    ScatterPlot(data)

    SurfacePlot(func, data, fittedParameters)

    ContourPlot(func, data, fittedParameters)

    print('fitted parameters', fittedParameters)

    modelPredictions = func(data, *fittedParameters)

    absError = modelPredictions - zData

    SE = numpy.square(absError) # squared errors

    MSE = numpy.mean(SE) # mean squared errors

    RMSE = numpy.sqrt(MSE) # Root Mean Squared Error, RMSE

    Rsquared = 1.0 - (numpy.var(absError) / numpy.var(zData))

    print('RMSE:', RMSE)

    print('R-squared:', Rsquared)

    fi = fittedParameters

    xData = df.loc[:,'vx']

    yData = df.loc[:,'vy']

    zData = df.loc[:,'dr']

    # place the data in a single list

    data = [xData, yData, zData]

    t = fi[0]*data[0]**5+fi[1]*data[0]**4+fi[2]*data[0]**3+fi[3]*data[0]**2+fi[4]*data[0]+fi[5]*data[1]**5+fi[6]*data[1]**4+fi[7]*data[1]**3+fi[8]*data[1]**2+fi[9]*data[1]+fi🔟*(data[0]**5)*(data[1]**5)+fi[11]*(data[0]**4)*(data[1]**4)+fi[12]*(data[0]**3)*(data[1]**3)+fi[13]*(data[0]**2)*(data[1]**2)+fi[14]*(data[0])*(data[1])+fi[15]