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As we have learned previously, we will first have to vectorize this post before we predict its label as follows: >>> new_post_vec = vectorizer.transform([new_post]) >>> new_post_label = km.predict(new_post_vec)[0] Chapter 3 Now that we have the clustering, we do not need to compare new_post_vec to all post vectors. Instead, we can focus only on the posts of the same cluster. Let us fetch their indices in the original dataset: >>> similar_indices = (km.labels_==new_post_label).nonzero()[0] The comparison in the bracket results in a Boolean array, and nonzero converts that array into a smaller array containing the indices of the True elements. Using similar_indices, we then simply have to build a list of posts together with their similarity scores as follows: >>> similar = [] >>> for i in similar_indices: ... dist = sp.linalg.norm((new_post_vec - vectorized[i]).toarray()) ... similar.append((dist, dataset.data[i])) >>> similar = sorted(similar) >>> print(len(similar)) 44 We found 44 posts in the cluster of our post. To give the user a quick idea of what kind of similar posts are available, we can now present the most similar post (show_ at_1), the least similar one (show_at_3), and an in-between post (show_at_2), all of which are from the same cluster as follows: >>> show_at_1 = similar[0] >>> show_at_2 = similar[len(similar)/2] >>> show_at_3 = similar[-1] [ 69 ]
Clustering – Finding Related Posts The following table shows the posts together with their similarity values: Position Similarity Excerpt from post 1 1.018 BOOT PROBLEM with IDE controller Hi, I've got a Multi I/O card (IDE controller + serial/parallel interface) and two floppy drives (5 1/4, 3 1/2) and a Quantum ProDrive 80AT connected to it. I was able to format the hard disk, but I could not boot from it. I can boot from drive A: (which disk drive does not matter) but if I remove the disk from drive A and press the reset switch, the LED of drive A: continues to glow, and the hard disk is not accessed at all. I guess this must be a problem of either the Multi I/o card\nor floppy disk drive settings (jumper configuration?) Does someone have any hint what could be the reason for it. […] 2 1.294 IDE Cable I just bought a new IDE hard drive for my system to go with the one I already had. My problem is this. My system only had a IDE cable for one drive, so I had to buy cable with two drive connectors on it, and consequently have to switch cables. The problem is, the new hard drive\'s manual refers to matching pin 1 on the cable with both pin 1 on the drive itself and pin 1 on the IDE card. But for the life of me I cannot figure out how to tell which way to plug in the cable to align these. Secondly, the cable has like a connector at two ends and one between them. I figure one end goes in the controller and then the other two go into the drives. Does it matter which I plug into the "master" drive and which into the "Slave"? any help appreciated […] [ 70 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Page 33 and 34: Getting Started with Python Machine
Page 48 and 49: Learning How to Classify with Real-
Page 50 and 51: Chapter 2 We are using Matplotlib;
Page 52 and 53: Chapter 2 The last few lines select
Page 54 and 55: Chapter 2 error = 0.0 for ei in ran
Page 56 and 57: Chapter 2 We can play around with t
Page 58 and 59: Chapter 2 Features and feature engi
Page 60 and 61: Chapter 2 In the preceding screensh
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Page 66 and 67: Chapter 3 How to do it More robust
Page 68 and 69: Chapter 3 This means that the first
Page 70 and 71: Chapter 3 ... post = posts[i] ... i
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Page 91 and 92: Topic Modeling For those who are in
Page 93 and 94: Topic Modeling Sparsity means that
Page 95 and 96: Topic Modeling Although daunting at
Page 97 and 98: Topic Modeling … for tj,v in t:
Page 99 and 100: Topic Modeling Finally, we build th
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Page 103 and 104: Topic Modeling Topic modeling was f
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Chapter 6 Getting to know the Bayes
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Using Naive Bayes to classify Given
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Chapter 6 This denotation "" leads
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Chapter 6 Similarly, we do this for
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Chapter 6 A quick look at the previ
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Chapter 6 To keep our experimentati
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Chapter 6 Y = np.zeros(Y.shape[0])
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Chapter 6 ° ° Experiment with whe
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Chapter 6 We have to be patient whe
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Chapter 6 First, we define a range
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Chapter 6 Determining the word type
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Chapter 6 Successfully cheating usi
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Chapter 6 Our first estimator Now w
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Chapter 6 for d in documents: allca
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Regression - Recommendations You ha
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Chapter 7 The preceding graph shows
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Chapter 7 Root mean squared error a
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Penalized regression The important
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Chapter 7 P greater than N scenario
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Chapter 7 So, we can see that the d
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Chapter 7 Fortunately, scikit-learn
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[ 161 ] Chapter 7 The loading of th
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Chapter 7 Summary In this chapter,
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Regression - Recommendations Improv
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Weights Regression - Recommendation
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Classification III - Music Genre Cl
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Chapter 9 Matplotlib provides the c
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[ 185 ] Chapter 9
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Chapter 9 def create_fft(fn): sampl
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Chapter 9 ax.set_yticks(range(len(g
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Chapter 9 On the left-hand side gra
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[ 193 ] Chapter 9 Improving classif
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We get the following promising resu
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Chapter 9 Summary In this chapter,
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Computer Vision - Pattern Recogniti
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Dimensionality Reduction Sketching
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Dimensionality Reduction However, t
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Dimensionality Reduction To underst
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Dimensionality Reduction In order t
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Dimensionality Reduction Asking the
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Dimensionality Reduction n_ feature
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Dimensionality Reduction Sketching
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Dimensionality Reduction Limitation
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Dimensionality Reduction Now, MDS t
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Dimensionality Reduction Of course,
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Big(ger) Data • Your algorithms c
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Big(ger) Data sleep(4) return 2*x @
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Big(ger) Data Looking under the hoo
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Big(ger) Data def write_result(ofna
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Big(ger) Data Amazon Web Services i
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Big(ger) Data In EC2 parlance, a ru
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Big(ger) Data In this system, pip i
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Big(ger) Data Keys, keys, and more
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Big(ger) Data We can use the same j
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Where to Learn More about Machine L
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• Machined Learnings at http://ww
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Index A AcceptedAnswerId attribute
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F false negative 41 false positive
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inary matrix of recommendations, us
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sklearn.naive_bayes package 127 skl
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NumPy Beginner's Guide - Second Edi
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