spark machine learning example python

), though it's no better than Microsoft's Visual Studio, but it's much more than the one that comes with it-if it's written in C, Helpless is written in Java, startup speed huge slow ~ ~Recently turned over the book "Machine Learning in Action". The book uses Python to implement some machine

Python Machine Learning Practical tutorialsShare Network address--https://pan.baidu.com/s/1miib4og Password: WTIWThe course is really good, share to everyoneMachine Learning (machines learning, ML) is a multidisciplinary interdisciplinary subject involving probability theory

): # Extend the Input feature vector as a feature matrix linenum = featurematrix.shape[0] featurematrixin = Np.tile ( Featurevectorin, (linenum,1)) # Calculate the Euclidean distance between the matrix Diffmatrix = featurematrixin -Featurematrix Sqdiffmatrix = Diffmatrix * * 2 Distancevaluearray = Sqdiffmatrix.sum (Axis=1) Distancevaluearray = Distancevaluearray * * 0.5 return DistancevaluearrayUsed in the numpy of the more distinctive things. The practice is to first

, 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

classes in the data. - -Many, many more ... the the a total of 150 data samples the evenly distributed over 3 subspecies the 4 petals per sample, calyx shape Description - " " the the " " the 2 dividing the training set and the test set94 " " theX_train, X_test, y_train, y_test =train_test_split (Iris.data, the Iris.target, thetest_size=0.25,98Random_state=33) About - " "101 3 K Nearest Neighbor Classifier learning model and prediction102 " "10

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

!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

[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

============================================================================================ "Machine Learning Combat" series blog is Bo master reading " Machine learning Combat This book's notes, including the understanding of the algorithm and the Python code implementatio

First of all, to collect ...This article is for the author after learning Zhou Zhihua Teacher's machine study material, writes after the class exercises the programming question. Previously placed in the answer post, now re-organized, will need to implement the code to take out the part of the individual, slowly accumulate. Want to write a machine

()--------------------------------------------------------------------------------------------------------------- ---------------------------------------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