book machine learning python

references: The reference is the low-dimensional matrix returned. corresponding to the input parameters of two.The number of references two corresponds to the matrix after the axis is moved.The previous picture. Green is the raw data. Red is a 2-dimensional feature of extraction.3. Code Download:Please click on my/********************************* This article from the blog "Bo Li Garvin"* Reprint Please indicate the source : Http://blog.csdn.net/buptgshengod***********************************

), 15.0*np.array (DatingLabels)) the #plt.show () - the #Unit test of Func:autonorm () the #Normmat, ranges, minvals = Autonorm (Datingdatamat) the #print (Normmat)94 #print (ranges) the #print (minvals) the the datingclasstest ()98Classifyperson ()Output:Theclassifier came back with:3, the real answer Is:3The total error rate is:0.0%Theclassifier came back with:2, the real answer Is:2The total error rate is:0.0%Theclassifier came back with:1, the real answer is:1The total error rate is:0.0%.

Python3 Learning using the APIUsing the data set on the Internet, I downloaded him to a localcan download datasets in my git: https://github.com/linyi0604/MachineLearningCode:1 ImportNumPy as NP2 ImportPandas as PD3 fromSklearn.clusterImportKmeans4 fromSklearnImportMetrics5 6 " "7 K-Mean-value algorithm:8 1 randomly selected K samples as the center of the K category9 2 from the K sample, select the nearest sample to be the same category as yourself,

K-means Clustering algorithm Test: #-*-coding:utf-8-*-"""Created on Thu 10:59:20 2017@author:administrator"""" "There are eight major variable data on the average annual consumer spending of urban households in 31 provinces in 1999, with eight variables: food, clothing, household equipment supplies and services, health care, transportation and communications, cultural services for recreational education, residential and miscellaneous goods and services. The 31 provinces are c

, or K nearest neighbor (Knn,k-nearestneighbor) classification algorithm, is one of the simplest methods in data mining classification technology. The so-called K nearest neighbor is the meaning of K's closest neighbour, saying that each sample can be represented by its nearest K-neighbor.The core idea of the KNN algorithm is that if the majority of the k nearest samples in a feature space belong to a category, the sample also falls into this category and has the characteristics of the sample on

!accuracy:87.07%******************* SVM ********************Training took3831. 564000s!accuracy:94.35%******************* GBDT ********************In this data set, because the cluster of data distribution is better (if you understand this database, see its T-sne map can be seen.) Since the task is simple, it has been considered a toy dataset in the deep learning boundary, so KNN has a good effect. GBDT is a very good algorithm, in Kaggle and other bi

Python3 Learning using the APIA sample of a data structure of a dictionary type, extracting features and converting them into vector formSOURCE Git:https://github.com/linyi0604/machinelearningCode:1 fromSklearn.feature_extractionImportDictvectorizer2 3 " "4 dictionary feature Extractor:5 pumping and vectorization of dictionary data Structures6 category type features vectorization with 0 12 values using prototype feature names7 numeric type features r

1. Background In the future, the blogger will update the machine learning algorithm and its Python simple implementation regularly every week. Today's algorithm is the KNN nearest neighbor algorithm. KNN algorithm is a kind of supervised learning classifier class algorithm. What is supervised

Here is still to recommend my own built Python development Learning Group: 483546416, the group is the development of Python, if you are learning Python, small series welcome you to join, everyone is the software Development Party, not regularly share dry goods (only

you separate a room with a wall, you're trying to create two different populations in the same room. Similarly, decision trees are dividing the population into different groups as much as possible. For more information, see: Simplification of decision tree algorithms Python code 7, K mean value algorithm k– mean algorithm is a kind of unsupervised learning algorithm, it can solve the problem of clustering.

1. Background Decision Book algorithm is a kind of classification algorithm approximating discrete numbers, which is simpler and more accurate. International authoritative academic organization, Data Mining International conference ICDM (the IEEE International Conference on Data Mining) in December 2006, selected the ten classical algorithms in the field of mining, C4.5 algorithm ranked first. C4.5 algorithm is a kind of classification decision tree

[Email protected]:~# pip Install-u Scikit-learnNo problemSuccessfully installed scikit-learncleaning up ...Other workarounds see: http://www.xuebuyuan.com/1157602.htmlInstalling NETWORKXwget https://pypi.python.org/packages/source/n/networkx/networkx-1.10.tar.gz#md5= EB7A065E37250A4CC009919DACFE7A9DCD Networkx-1.10python setup.py InstallTest it:[Email protected]:~/networkx-1.10# pip listmatplotlib (1.3.1) networkx (1.10) numpy (1.8.2) pip (1.5.4) Scikit-learn ( 0.16.1) scipy (0.13.3) setuptools

the data after dimensionality reduction -Pca_svc =linearsvc () the #Learning - Pca_svc.fit (Pca_x_train, Y_train)WuyiPca_y_predict =pca_svc.predict (pca_x_test) the - #4 Model Evaluation Wu Print("accuracy of raw data:", Svc.score (X_test, y_test)) - Print("other ratings: \ n", Classification_report (Y_test, Y_predict, Target_names=np.arange (10). Astype (str ))) About $ Print("data accuracy rate after dimensionality reduction:", Pca_svc.score (Pca

1. Install Pipmkdir ~/vi ~/.pip/pip.conf[global]trusted-host=mirrors.aliyun.comindex -url=http://https://bootstrap.pypa.io/get-pip.pysudo python get---9.0. 1 from/usr/local/lib/python2. 7 2.7)2. Install the Machine learning PackageThe following installation package is not chaotic due to dependenciessudo Install sudo install sudo install sudo install scipyError:S

print ' Best Feature index:\t ', bestfeatureindex print ' Best thresh old:\t\t ', Bestthreshold ' return{' Dim ': Bestfeatureindex,' Thresh ': Bestthreshold,' accuracy ': Bestaccuracy} def Apply_model(Features,labels,model):Prediction = (features[:,model[' Dim ']] > model[' Thresh '])returnPrediction#-----------Cross validation-------------Error =0.0 forEiinchRange (len (irisfeatures)):# Select All and the one at position ' ei ':Training = Np.ones (len (irisfeatures), bool) Training[ei] =Fal

()--------------------------------------------------------------------------------------------------------------- ---------------------------------------At lastCode SummaryImport NumPy as Npimport cv2from matplotlib import pyplot as PltX = Np.random.randint (25,50, (25,2)) Y = Np.random.randint (6 0,85, (25,2)) Z = Np.vstack ((x, y)) # Convert to np.float32z = Np.float32 (Z) plt.hist (z,100,[0,100]), Plt.show () # define Criteria and apply Kmeans () criteria = (CV2. Term_criteria_eps + CV2. Ter

Print "Performing greedy feature selection ..." score_hist = []n = 10good_features = Set ([]) # greedy Feature selection LOOPW Hile Len (score_hist) if f not in good_features: feats = List (good_features) + [f] Xt = Sparse.hstack ([xts[j] for J in feats]). TOCSR () C5/>score = Cv_loop (Xt, y, model, N) Scores.append ((score, F)) print "Feature:%i Mean AUC:%f"% (f, score) g Ood_features.add (sorted (scores) [ -1][1]) Score_hist.append (sorted

=true) # drop useless columns and create LABELSIDX = test.id.values.astype (int) test = Test.drop ([' id ', ' tube_assembly_id ', ' quote_date '), Axis = 1) labels = Train.cost.valuestrain = Train.drop ([' Quote_date ' , ' cost ', ' tube_assembly_id '], Axis = 1) # Convert data to NumPy Arraytrain = Np.array (train) test = Np.array (test)From:kaggle Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced. Ma