machine learning with python cookbook pdf

" from NumPy Import *def loadsimpdata (): Datmat = Matrix ([[[1., 2.1], [2., 1.1], [1.3, 1.], [1., 1.], [2., 1.]]) Classlabels = [1.0, 1.0, -1.0, -1.0, 1.0] return datmat,classlabelsdef loaddataset (fileName): #general function to Parse tab-delimited Floats numfeat = Len (open (FileName). ReadLine (). Split (' \ t ')) #get number of fields Datamat = [ ]; Labelmat = [] fr = open (fileName) for line in Fr.readlines (): Linearr =[] curline = Line.strip (). Split (' \ t ') for I in

such as the followingHere is an example of a Python implementation:#-*-coding:cp936-*-"Created on Nov, 2010Adaboost was short for Adaptive Boosting@author:peter" from NumPy Import *def loadsimpdata (): Datmat = Matrix ([[[1., 2.1], [2., 1.1], [1.3, 1.], [1., 1.], [2., 1.]]) Classlabels = [1.0, 1.0, -1.0, -1.0, 1.0] return datmat,classlabelsdef loaddataset (fileName): #general function to Parse tab-delimited Floats numfeat = Len (open (File

Keras is a python library for deep learning that contains efficient numerical libraries Theano and TensorFlow. The purpose of this article is to learn how to load data from CSV and make it available for keras use, how to model the data of multi-class classification using neural network, and how to use Scikit-learn to evaluate Keras neural network models.Preface, the concept description of two classificatio

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