logistic regression python code

gradient descent methods ①stochastic descent random gradient descentQuite unstable, try to turn the study rate down a little bit.The speed is fast, the effect and the stability are poor, need very small study rate②mini-batch descent small batch gradient descentNormalization/NormalizationFloating is still relatively large, let's try to standardize the data by subtracting its mean value by its attributes (in columns) and then dividing by its variance. Finally, the result is that all data is aggre

, the classifier performance has a large number of hints, the following figure: The code to draw this picture is as follows: #-*-Coding:utf-8-*-# import Pandas package, alias for PD import pandas as PD # import NumPy Toolkit, renamed to NP import NumPy as NP # import Matplotlib Toolkit Lot and named as PLT import Matplotlib.pyplot as PLT # import Sklearn in the logistic

This article mainly describes the Python implementation of the method of logistic regression example, this is a machine learning course of an experiment, organized to share to everyone, the need for friends can refer to the study, down to see it together. The principle of this paper is very simple, the optimization method is to use gradient descent. There are te

("Class") Plt.title ("Linear Fit") Plt.grid (True, linestyle= '-'-', color= ' 0.75 ') # Creates a row of two-column sub-graphs in the image of the second figure Plt.subplot (1, 2, 2) plt.scatter (x, y, c=y) plt.plot (x, Lr_model (LOGCLF, X). Ravel (), "O", color= "C") Plt.plot (XS, Lr_model (LOGCLF, xs). Ravel (), "-", color= "green") Plt.xlabel ( "Feature value") Plt.ylabel ("Class") Plt.title ("Logistic Fit") Plt.grid (True, linestyle= '-', color=

Summary1. The computational cost of logistic regression is not high, it is a very common classification algorithm. The centralized logistic regression classifier based on random gradient rise can support online learning.2. However, the disadvantage of the logistic

Logistic regression (Logistic regression) is a common machine learning method used in the industry to estimate the possibility of something. For example, a user may buy a product, a patient may suffer from a disease, and an advertisement may be clicked by the user. (Note: "possibility", not the "probability" in mathema

Code:1 ImportNumPy as NP2 fromSklearnImportDatasets3 fromSklearn.linear_modelImportlogisticregression4 ImportMatplotlib.pyplot as Plt5 6 __author__='Zhen'7 8Iris =Datasets.load_iris ()9 Ten forIinchRange (0, 4): Onex = iris['Data'[:, I:i+1]#Get Training Data Ay = iris['Target'] - -Param_grid = {"Tol": [1e-4, 1e-3, 1e-2],"C": [0.4, 0.6, 0.8]} the -Log_reg = Logisticregression (multi_class='OVR', solver='SAG', max_iter=1000)#OVR: Two categories - l

Original address: http://blog.csdn.net/abcjennifer/article/details/7716281This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Suppo

#-*-Coding:utf-8-*-Import NumPyDef loaddataset ():Return Datamat,labelmatdef sigmoid (InX):Return 1.0/(1+numpy.exp (-inx))def gradascent (datamatin,classlabels):Datamatrix=numpy.mat (Damamatin)Labelmat=numpy.mat (Classlabels). Transpose ()#上升梯度alpha=0.01#迭代次数maxcycles=500#初始回归向量M,n=numpy.shape (Datamatrix)Weights=numpy.ones ((n,1))for k in range (Maxcycles):H=sigmoid (datamatrix*weights)Error= (labelmat-h)Weights=weights+alpha*datamatrix.transpose () *errorPassReturn weightsdef test ():Dataarr,l

[21]): Errorcount + = 1 #计算错误率 errorrate = (Float (errorcou NT)/numtestvec) print "The error rate of this test is:%f"% errorrate return errorratedef multitest (): numtests = 10; errorsum=0.0 for K in range (numtests): Errorsum + = Colictest () print "After%d iterations the average error R ATE is:%f "% (numtests, errorsum/float (numtests))Implementation results:The error rate of this test is:0.358209the error rate of this test is:0.417910the error rate of this test is:0.268657th E error r

Test questions:Code Description:1. In main I used an input file to represent the input, which should be removed when testing2. The following functions are the calculation of the predicted values, the calculation of costfunction, the implementation of the Luo series regression3. Specifically similar to linear regression, can refer to the gradient descent of linear regressionThe code is as follows:#include Op

classification, and get the probability of two classification cases. Here's a great post. [6] recommended, elaborated the logic regression mentality. When deriving the problem of multiple classifications, it is assumed that wt1x+b1=p (Y=1|X) p (y=k|x) w1tx+b1=p (y=1|x) p (y=k|x), Wt2x+b2=p (y=2|x) p (y=k|x) w2tx+b2=p (y=2|x) (Y=K|X) p )... And so on, then deduced P (y=k|x) =11+∑k−1k=1ewtkx P (y=k|x) =11+∑K=1K−1EWKTX, P (y=1|x) =ewt1x1+∑k−1k=1ewtkx P

and linear regression appear to be the same:But its hypothetical function is different.Linear regression assumption function:Logical regression assumption function:6. Advanced Optimization In addition to the gradient descent method, there are conjugate gradient method, BFGS (variable scale method) and L-BFGS (limited variable scale method), the advantage of thes

1-Questions raised 2-Logistic regression 3-Theoretical derivation 4-python/spark implementation1 #-*-coding:utf-8-*-2 fromPysparkImportSparkcontext3 fromMathImport*4 5theta = [0, 0, 0]#Initial theta Value6Alpha = 0.001#Learning Rate7 8 definner (x, y):9 returnSUM ([i*j forI,jinchzip (x, y)])Ten One deffunc (LST): AH = (1 + exp (-i

is performed for all sample $j$ features.Linear regression least squares and gradient descent methods Python code is as follows:#-*-coding:utf-8-*-"""Created on Fri Jan 13:29:14 2018@author:zhang"""ImportNumPy as NP fromSklearn.datasetsImportLoad_bostonImportMatplotlib.pyplot as Plt fromSklearn.cross_validationImportTrain_test_split fromSklearnImportpreprocessin

Tomorrow the first class 8.55 only, or the things you see today to tidy up.Today is mainly to see Ng in the first few chapters of the single-line regression, multi-linear regression, logistic regression of the MATLAB implementation, before thought those things understand well, but write

, ycord1, s = -, C =' Red ', marker =' s ') Ax.scatter (Xcord2, ycord2, s = -, C =' Blue ') x = Arange (-3.0,3.0,0.1) y = (-w[0]-w[1] * x)/w[2] Ax.plot (x, y) Plt.xlabel (' x1 '); Plt.ylabel (' x2 '); Plt.show () Plotbestsplit (W.geta ())As a result, you can see that the classification results using logistic regression are quite good, even though there are three or four sample points that have been wrongly

matrix operation. The variable h is not a number but a column vector, the number of woodlands in the column vector equals the number of samples, here is 100. Correspondingly, the operation Datamatrix * weights more than once the product calculation, in fact, the operation contains 300 times the product. 5.2.3 Analyzing data: Drawing decision boundariesRun the code in Listing 5-2, and at the Python pr

http://blog.csdn.net/hechenghai/article/details/46817031The main reference to statistical learning methods, machine learning in combat to learn. below for reference.In the first section, the difference between logistic regression and linear regression is that linear regression is based on the linear superposition of th

hypothetical function of logistic regression is as follows, and the linear regression hypothesis function is just. Logistic regression is used to classify the 0/1 problem, which is the two value classification problem that the prediction result belongs to 0 or 1. This assu