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Machine learning (--regularization:regularized) Linear regression

Machine learning (--regularization:regularized) Linear regression Gradient descent Without regularization With regularization Θ0 is the same as the original, no regularization. The θ1-n is slightly smaller than the original (1-αλ⁄m) Normal equation Witho

The linear regression of Python machine learning

# Linear regression least squaresFrom Sklearn import Linear_modelImport SysImport Tushare as TSImport Matplotlib.pyplot as PltImport Pandas as PDImport Sklearn.metrics as SMSh=ts.get_hist_data (' sh '). Sort_index () #获取上证指数每日数据 and sorted by time indexPf=ts.get_hist_data (' 600000 '). Sort_index () #获取浦发银行数据 and sorted by time indexsh[' re ']=np.log (sh[' close ']/sh[' Close '].shift (1)) #计算上证指数收益率pf[' re

Unary linear regression to R language

(https://mirrors.tuna.tsinghua.edu.cn/CRAN/) After download good R open, you can enter the command, as below, I enter> Y=c (61,57,58,40,90,35,68) indicates that a y vector is created, and the value of the vector is the content after C> y echo y[1] 61 57 58 40 90 35 68> X=c (170,168,175,153,185,135,172) create an x vector> x Echo X[1] 170 168 175 153 185 135 172>> Plot (x, y), y to the left ordinate, and scatter plot.> Z=lm (y~x+1) a linear

Lecture3 Linear and Logistic Regression

In this lesson, we talk about the example of the regression of the downline, and introduce several common optimization algorithms. After the linear fitting of the data, it is found that the error is large, so Huber loss is proposed.Huber loss is in robust regress (robust regression. ) is used in the loss function, compared to the square error, the discrete value

R Language Linear model GLM () Logistic regression model

R Language Generalized linear Model GLM () functionGLM (formula, family=family.generator, Data,control = List (...))Formula data relationships, such as y~x1+x2+x3Family: Each response distribution (exponential distribution family) allows various correlation functions to correlate the mean with the linear predictor.Common family: Binomal (link= ' logit ')--the response variable is subject to two distributio

Multiple linear regression Exercises

); theta = Theta-alpha (alpha_i) .*grad; end plot (0:49, Jtheta ( 1:50), char (Plotstyle (alpha_i)), ' LineWidth ', 2)% be sure to convert by Char function hold on if (1 = = Alpha (alpha_i))% The experiment found that Alpha was1 o'clock the effect is best, then the theta value after the iteration is the desired value theta_grad_descent = theta endendlegend (' 0.01 ', ' 0.03 ', ' 0.1 ', ' 0.3 ', ' 1 ', ' 1.3 '); Xlabel (' Number of iterations ') ylabel (' cost function ')% below is the pred

Linear regression--cost function

}\left ({{x^{(i)}}} \right)-{y^i}} \right)}^2}} \]Target is \[\mathop {\min imize}\limits_{{\theta _0},{\theta _1}} j\left ({{\theta _0},{\theta _1}} \right) \]This cost function is also called the squared error cost function (squared error function)Summarize:Hypothesis: \[{h_\theta}\left (x \right) = {\theta _0} + \theta {x}\]Parameters: (θ0,θ1)Cost Functions: \[j\left ({{\theta _0},{\theta _1}} \right) = \frac{1}{{2m}}\sum\limits_{i = 1}^m {{\left} ({{H_\theta}\l EFT ({{x^{(i)}}} \right)-{y^i}

TensorFlow Implementing Linear Regression

, 1)) A #Pre-valuation atPreY = w * x +b - - #loss value: The mean variance between the pre-estimate and the actual value -Loss = Tf.reduce_mean (Tf.square (PreY-y)) - #Optimizer: Gradient Descent method -Optimizer =Tf.train.GradientDescentOptimizer (learnrate) in #Training: Minimizing loss function -Trainer =optimizer.minimize (loss) to + With TF. Session () as Sess: - Sess.run (Tf.global_variables_initializer ()) the #print initial random coefficients * Print(

Machine Learning-week 2-multivariate Linear Regression

, meaning you have only 10 data, but there are 100 features, obviously, the data is not enough to cover all the features.You can delete some features (keep only data-related features) or use regularization.Exercises1.Don't know how to use both methods at the same time, are these two methods sequential related?Use dividing by the rangeRange = Max-min = 8836-4761 = 4075Vector/range after change to1.94381.27212.16831.1683For the above use mean normalizationAVG = 1.6382Range = 2.1683-1.1683 = 1X2 (4

[Note] linear regression & Gradient Descent

I. Summary Linear Regression Algorithms are a type of supervised learning algorithm used for Numerical Prediction of continuous functions. After preliminary modeling, the process determines the model parameters through the training set to obtain the final prediction function. Then, the predicted value can be obtained by inputting the independent variable.Ii. Basic Process 1. Preliminary modeling. Determine

TensorFlow (c) linear regression algorithm for L2 regular loss function with TensorFlow

(train_step,feed_dict={x_data:rand_x,y_data:rand_y}) Temp_loss=sess.run (loss,feed_dict={x_data:rand_x,y_data:rand_y})#Add a recordloss_rec.append (Temp_loss)#Print if(i+1)%25==0:Print('Step:%d a=%s b=%s'%(I,str (Sess.run (A)), str (Sess.run (b) )))Print('loss:%s'%str (temp_loss))#decimation Factor[slope]=Sess.run (A)Print(slope) [Intercept]=Sess.run (b) Best_fit=[] forIinchX_vals:best_fit.append (Slope*i+intercept)#x_vals shape (none,1)Plt.plot (X_vals,y_vals,'o', label='Data') Plt.plot (X_

[Exercise] linear regression, gradient descent algorithm

+1.999936002667351 -1.9999628815470636 +1.9999784712972968 A1.999987513352432 at1.9999927577444105 -1.999995799491758 -1.9999975637052196 -1.9999985869490273 -1.9999991804304358 -1.9999995246496527 in1.9999997242967986 -1.9999998400921433 to1.9999999072534431 +1.999999946206997 -1.9999999688000583 the1.9999999819040337 *1.9999999895043397 $1.999999993912517Panax Notoginseng1.9999999964692599 -1.9999999979521708 the1.999999998812259 +1.9999999993111102 A1.9999999996004438 the1.9999999997682574 +

Linear regression (least squares,)

price of a house is whether the area is more important or the room orientation is more important. We make x0 = 1, we can use vectors to represent In the above formula, once Theta is determined, then our straight line is determined, and we are able to forecast the house price. So the job we're going to do is to determine theta. The value of θ can have countless, how should we choose θ? 3. Model Establishment-least squares O

[Stanford] II. Supervised Learning: Linear Regression

Supervised Learning Learn a function H: X → y H is called a hypothesis. 1. Linear Regression In this example, X is a two-dimensional vector, x1 represents living area, and x2 represents bedrooms. Functions/hypotheses H Set X0 = 1. Now, given a training set, how do we pick, or learn, the parameters θ? Now it is used to evaluate the θ parameter. One reasonable method seems to be to make h (x)

Lesson One: Linear regression algorithm Introduction to the detailed

Mathematical model, if need to bank loans, according to the loan amount calculation, there are age, wages, assets and other factors, this translates into mathematics is:Now we are to fit a plane to make a diagram, but this fitting theoretical value and the results of the real difference, now we through the statistical error calculation method, we believe that each error is independent of the same distribution, and obey the mean value of 0 variance is the Gauss distribution of the square. Indepen

Unit employee Address Book Management system (Application of linear table)

;Switch (in){Case 0:BreakCase 1://Creating a new Address BookCreateList (L);//inverse-order establishment of linear tablecout System ("pause");BreakCase 2: {//findGetelem (L);System ("pause");Break}Case 3://ModifyReviselist (L);System ("pause");cout BreakCase 4://AddListinsert (L);cout System ("pause");BreakCase 5:Printlist (L);Listdelete (L);cout System ("pause");break;//Output All informationCase 6:Printlist (L);System ("pause");BreakCase 7:cout Sys

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