Python pure code for fitting linear regression via neural networks

# -*- coding: utf-8 -*-
import numpy as np
import  as plt
class SimpleDataReader(object):
    def __init__(self, data_file):
        self.train_file_name = data_file
        self.num_train = 0
         = None
         = None
    # read data from file
    def ReadData(self):
        # data = (self.train_file_name)
        #  = data["data"]
        #  = data["label"]
         = ([0.95, 3, 4, 5.07, 6.03, 8.21, 8.85, 12.02, 15], dtype=float)
         = ([5.1, 8.7, 11.5, 13, 15.3, 18, 21, 26.87, 32.5], dtype=float)
        self.num_train = [0]
        #end if
    # get batch training data
    def GetSingleTrainSample(self, iteration):
        x = [iteration]
        y = [iteration]
        return x, y
    def GetWholeTrainSamples(self):
        return , 
class NeuralNet(object):
    def __init__(self, eta):
         = eta
         = 0
         = 0
    def __forward(self, x):
        z = x *  + 
        return z
    def __backward(self, x,y,z):
        dz = z - y                  # The original error was: dz = x * (z - y)
        db = dz
        dw = dz
        return dw, db
    def __update(self, dw, db):
         =  -  * dw
         =  -  * db
    def train(self, dataReader):
        for i in range(dataReader.num_train):
            # get x and y value for one sample
            x,y = (i)
            # get z from x,y
            z = self.__forward(x)
            # calculate gradient of w and b
            dw, db = self.__backward(x, y, z)
            # update w,b
            self.__update(dw, db)
            # end for
    def inference(self, x):
        return self.__forward(x)
if __name__ == '__main__':
    # read data
    sdr = SimpleDataReader('')
    ()
    # create net
    eta = 0.1
    net = NeuralNet(eta)
    (sdr)
    # result
    print("w=%f,b=%f" %(, ))
    # Plotting section
    trainX,trainY = ()
    fig = ()
    ax = fig.add_subplot(111)
    # Scatterplotting
    (trainX,trainY)
    # Plotting linear regression
    x = (0, 15, 0.01)
    f = (, excluded=['x'])
    (x,f(x),color='red')
    # Show charts
    ()

#1. Data acquisition
import pandas as pd
data = pd.read_excel('Capacity Data Sheet.xlsx')
x = [:20,:4]
y = [:20,4:]
#2. Introducing the linear regression module, referred to as LR.
from sklearn.linear_model import LinearRegression as LR
lr = LR()             # Create linear regression model classes
(x,y)         #fitting
slr=(x,y)   #Coefficient of determination R^2
c_x=lr.coef_         Regression coefficients corresponding to #x
c_b=lr.intercept_   # Regression coefficient constant term
#3. Projections
x1 = [20:,:4]
r1=(x1)    # Use self-contained function prediction
#print('The x regression coefficient is:',c_x)
#print('The constant term of the regression coefficient is:',c_b)
#print('The coefficient of determination is:',slr)
#print('The sample predicted value is:',r1)
n=list(data["Kilometers of passenger traffic (10,000)"])
(r1[:,0])
num=(n).dropna()
g=list(data["Kilometers of freight (tons)"])
(r1[:,1])
gravity=(g).dropna()
import pandas as pd
import  as plt  # Import the pyplot module from the plotting library, and call it plt for short.
# Construct the horizontal and vertical axis data columns required for plotting.
# Plotting linear graphs on the figure interface
['-serif'] = 'SimHei'     #Set the font to SimHei
(1)
(data["Year"],num,'r*--')  #Red "*" continuous chart.
('Date')
('Kilometers of passenger traffic (10,000)')
('Kilometer Passenger Traffic (10,000) Trend')
(data["Year"],rotation = 45)
('myfigure1')
(2)
(data["Year"],gravity,'b*--')  #Red "*" continuous chart.
('Date')
('Kilometers of freight (tons)')
('Kilometer freight volume (tons) trend')
(data["Year"],rotation = 45)
('myfigure2')
from sklearn.neural_network import MLPRegressor 
clf = MLPRegressor(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=8, random_state=1) 
(x, y);   
rv=(x,y)
r2=(x1)   
print('The sample predicted values are:',r2)
n2=list(data["Kilometers of passenger traffic (10,000)"])
(r2[:,0])
num2=(n2).dropna()
g2=list(data["Kilometers of freight (tons)"])
(r2[:,1])
gravity2=(g2).dropna()