python machine learning library

-za-z]"," ", Sent.lower (). Strip ()). Split () in sentences.append (temp) - to returnsentences + - #The sentences in the long news are stripped out for training . thesentences = [] * forIinchx: $Sentence_list =news_to_sentences (i)Panax NotoginsengSentences + =sentence_list - the + #Configure the dimension of the word vector ANum_features = 300 the #the frequency of the words that are to be considered +Min_word_count = 20 - #number of CPU cores used in parallel computing $Num_workers =

#岭回归主要是弥补在数据中出现异常值时, improve the stability of linear model, that is, robustness robustImport Pandas as PDImport NumPy as NPImport Matplotlib.pyplot as PltFrom Sklearn import Linear_modelImport Sklearn.metrics as SM#直接拿最小二乘法数据Ridgerg=linear_model. Ridge (alpha=0.5,fit_intercept=true,max_iter=10000) #alpha nearer to 0, the more the ridge regression approached the linear regression.Ridgerg.fit (X_train,y_train) #训练模型Y_train_pred=ridgerg.predict (X_train) #模型y值Y_test_pred=ridgerg.predict (x_test) #模

Python Kmeans clustering is relatively simple, first requires the import NumPy, from the Sklearn.cluster import Kmeans module:Import NumPy as NP from Import KmeansThen read the TXT file, get the corresponding data and convert it to numpy array:X == open ('rktj4.txt') for in f: = Re.compile ('\s+') x.append ([Float (Regex.Split (v) [3]), float ( Regex.Split (v) [6= Np.array (X)Set the number of classes and cluster:N_clusters = 5= Kmeans (n_clust

Maximumlikelihood (p=w): H,t=defineparam () f1=factorial (h+t)/(factorial (H) *factorial (T)) f2= (p**h) * ((1.0-p) **t) return F1*F2 def factorial (x): return reduce (lambda x,y:x*y,range (1,x+1)) achieve the effect, corresponding to the above example, when h=49,t=31, is the probability of P=2/3 probabilitiesCode Address: Please click on my/********************************* This article from the blog "Bo Li Garvin"* Reprint Please indicate the sourc

It is mentioned in this series that using Python to start machine learning (3: Data fitting and generalized linear regression) mentions the regression algorithm for numerical prediction. The logical regression algorithm is essentially regression, but it introduces a logical function to help classify it. The practice found that the logical regression in the field

[0]print ("k=", K, "b = ", b) Print (" Cost: "+str (para[1)) print (" Solved fit line is: ") print (" y= "+str (rOund (k,2)) + "x+" +str (Round (b,2)) "'" plot to see the fit effect. Matplotlib default does not support Chinese, label set Chinese words need to be set separately if the error, change into English can be "#画样本点plt. Figure (Figsize= (8,6)) # #指定图像比例: 8:6plt.scatter (Xi,yi, Color= "Green", label= "Sample Data", linewidth=2) #画拟合直线x =np.linspace (0,12,100) # #在0-15 Direct Draw 100 cons

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

This article is translated from awesome-machine-learning by bole online-toolate. Welcome to the technical translation team. For more information, see the requirements at the end of the article. This article has compiled some frameworks, libraries, and software (sorted by programming language) in the machine learning fi

This article has compiled some frameworks, libraries, and software (sorted by programming language) in the machine learning field ).C ++ Computer Vision CCV-Machine Vision Library Based on C Language/provided Cache/core, novel machine vision

. After writing this perceptual machine, found that machine learning is not so difficult to imagine, in fact, as long as know how to calculate it can be (at present more superficial understanding, perhaps I have not touched the difficult place), and Python, the machine

learning in Hadoop that you can learn by yourself. If you are a novice in machine learning and big data learning, stick to learning Weka and learn a library wholeheartedly. Scikit Learn: This is a

Learning notes for "Machine Learning Practice": two application scenarios of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- After learning the implementation of the k-Nearest Neighbor Algorithm, I tested the k-

Learning notes for "Machine Learning Practice": Implementation of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- The main learning and research tasks of the last semester were pattern recognition, signal theor

Machine Learning 3, machine learning K-Nearest Neighbor Algorithm for machine learning in PythonPreface I recently started to learn machine learnin