machine learning with python cookbook pdf

-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

See original book 2.1-2.2 sectionThe new dataset is like a wrapped gift, filled with promise and hope!But until you open it, it remains mysterious!I. Structure and terminology of the underlying problem, characteristics of the machine learning data setTypically, rows represent instances, columns represent attribute characteristicsproperty, the data used in the instance for predictionOther Name: Predictive fa

[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

') trainset= []; Trainlabels = [] forLineinchtrainfile.readlines (): line_s= Line.strip (). Split ('\ t') Linearr= [] forIinchRange (21): Linearr.append (float (line_s[i)) trainset.append (Linearr) trainlabels.append (float (line_s[ -1])) Sigma= StocGradAscent1 (trainset,trainlabels,500) error_cnt=0.0;numtestvec =0 forLine1inchtestfile.readlines (): Numtestvec+=1line_s1= Line1.strip (). Split ('\ t') lineArr1= [] forJinchRange (21): Linearr1.append (float (line_s1[j)))ifInt (

Full Stack Engineer Development Manual (author: Shangpeng) Python Data Mining Series tutorials GBDT's algorithm reference: https://blog.csdn.net/luanpeng825485697/article/details/79766455 Gradient boosting is a boosting method, and its main idea is that each time a model is established, the gradient descent direction of the model loss function is established. Loss function is the performance of evaluation model (generally fit degree + regular term), t

Reference: http://my.oschina.net/u/175377/blog/84420First: Use Sklearn to import very simple famous flower data--Anderson Iris Floral Data set.We have some measurements of the size of 150 irises: sepals length, width, petal length and width. There are also their sub-genus: The iris setosa, the Iris versicolor, and the Iris virginica Virginia. The data is stored in the. Data entry and is an array (N_samples, n_features). The type of each observation object is stored in the. Target property of the