#The simple linear regression equation keyboard code#
I will post the linear regression program written in Python language below, which can be combined to compare the similarities and differences.Īddress algorithm array assembly attribute Browser c Character string Client code command configuration file container data Database Definition Edition element Example file function java javascript Journal link linux Memory method Model Modular mysql node object page parameter php Plug-in unit project python Route source code The server Thread time user Recent Posts Later, it will be implemented in C language in an orderly manner, such as multiple linear regression, binary classification problem, multi classification problem and even convolutional neural network. There is a large space for the program to be improved, such as the visualization of data and models, which can intuitively display the training effect of data and models. If I really want to write a program to realize the functions, I recommend using Python language with tensorflow, pytoch and other deep learning platforms to realize the functions I want. This is just the program I use to practice my hand. At the same time, I also tried the mechanical keyboard I just bought (it’s really comfortable to use). This time, I just realized the most basic function of simple linear regression. It needs many attempts to find appropriate values, and the same is true for parameter initialization. is a job that requires high experience of programmers. The setting of learning rate, training rounds, etc. Super parameter setting and parameter initialization When the parameter value is less than 1, it will no longer change.
Later, when checking the code, it was found that the data type of some parameters was incorrectly set to integer in the optimization function. Pay attention to the data typeĭuring the initial operation of the program, the parameters will not change until more than ten rounds of training, and will remain unchanged until the 1000th round. However, when running the program that is not a function, there is no real-time loss function result to evaluate the training effect of the model, which may lead to the model going further and further on the wrong road. I just need to use the formula after derivation to optimize the relevant parameters in the process of gradient descent. In the process of coding, I think I don’t need to write a loss function alone. After the updated values of all parameters are calculated by the gradient descent method, all parameters should be updated at the same time. The parameters in the model need to be updated synchronously. The following is a summary of the problems needing attention in the preparation process: 1. The figure above shows the function calling relationship of the whole program. The batch gradient descent method is used to train the model, and the square loss function is used as the loss function. Step 3 Then: Suppose both x 1 and x 2 made it into the two-predictor stepwise model and remained there. If the t -test P -value for 1 0 has become not significant that is, the P -value is greater than R 0.15 remove x 1 from the stepwise model. The training data is 100 random data generated by using random numbers and saved in the array. That is, check the t -test P -value for testing 1 0. The model contains two parameters: variable coefficient W and offset Q. The linear regression realized this time is a simple linear regression of single variable. It is a regression analysis that models the relationship between independent variables and dependent variables.
Step 3 Square the differences and add them all up. Step 2 Calculate the difference between each x and average x. To do so, follow these steps: Step 1 Calculate the average of the x variable. Linear regression is the most basic and widely used model in machine learning and statistics. Apart from the above linear regression formula, you can also arrange your data and calculate regression by hand. This paper summarizes the problems and experiences encountered in the process of using C language to realize univariate linear regression. from scipy.In order to test my previous mastery of linear regression in machine learning and find out my problems in learning, I simply realized univariate simple linear regression in C language. Optimal values for the parameters so that the sum of the squared residuals of sigmoid (xdata, *popt) – ydata is minimized.
We can use curve_fit which uses non-linear least squares to fit our sigmoid function, to data. How we find the best parameters for our fit line? Lets first normalize our x and y: # Lets normalize our data Our task here is to find the best parameters for our model. #plot initial prediction against datapoints Y_pred = sigmoid(x_data, beta_1, beta_2) Lets look at a sample sigmoid line that might fit with the data: beta_1 = 0.10 Now, let’s build our regression model and initialize its parameters.
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