软件概览
clj-ml
A machine learning library for Clojure built on top of Weka and friends.
This library (specifically, some dependencies) requires Java 1.7+.
Installation
Installing from Clojars
[cc.artifice/clj-ml "0.8.5"]
Installing from Maven
(add Clojars repository)
<dependency>
<groupId>cc.artifice</groupId>
<artifactId>clj-ml</artifactId>
<version>0.8.5</version>
</dependency>
Supported algorithms
Filters
- Discretization (supervised, unsupervised, PKI)
- Nominal to binary (supervised, unsupervised)
- Numeric to nominal
- String to word vector
- Attribute manipulation (reorder, add, remove range, remove percentage, etc.)
- Resample (supervised, unsupervised)
- Replace missing values with mean (numeric attributes) or mode (nominal attributes)
Classifiers
- k-Nearest neighbor
- Decision trees: C4.5/J4.8, Boosted stump, Random forest, Rotation forest, M5P
- Naive Bayes
- Multilayer perceptrons
- Support vector machines (grid-based training), SMO, Spegasos
- Raced Incremental Logit Boost
Regression
- Linear
- Logistic
- Pace
- Additive gradient boosting
Clusterers
- k-Means
- Cobweb
- Expectation-maximization
Usage
API documenation can be found here.
I/O of data
user> (use 'clj-ml.io)
nil
user> (def ds (load-instances :arff "file:///home/josh/git/clj-ml/iris.arff"))
#'user/ds
user> ds
#<Instances @relation iris
@attribute sepallength numeric
@attribute sepalwidth numeric
@attribute petallength numeric
@attribute petalwidth numeric
@attribute class {Iris-setosa,Iris-versicolor,Iris-virginica}
@data
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
...
user> (def ds (load-instances :arff "http://repository.seasr.org/Datasets/UCI/arff/iris.arff"))
#'user/ds
user> (save-instances :csv "iris.csv" ds)
nil
user> (println (slurp "iris.csv"))
sepallength,sepalwidth,petallength,petalwidth,class
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
...
user> (def ds (load-instances :csv "file:///home/josh/git/clj-ml/iris.csv"))
#'user/ds
user> ds
#<Instances @relation stream
@attribute sepallength numeric
@attribute sepalwidth numeric
@attribute petallength numeric
@attribute petalwidth numeric
@attribute class {Iris-setosa,Iris-versicolor,Iris-virginica}
@data
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5,3.4,1.5,0.2,Iris-setosa
Working with datasets
user> (use 'clj-ml.data)
nil
user> (def ds (make-dataset "my-name" [:length :width {:style nil} {:kind [:good :bad]}]
[[12 24 "longish" :good]
[8 5 "shortish" :bad]]))
#'user/ds
user> ds
#<ClojureInstances @relation my-name
@attribute length numeric
@attribute width numeric
@attribute style string
@attribute kind {good,bad}
@data
12,24,longish,good
8,5,shortish,bad>
user> (dataset-seq ds)
(#<Instance 12,24,longish,good> #<Instance 8,5,shortish,bad>)
user> (map instance-to-map (dataset-seq ds))
({:kind :good, :style "longish", :width 24.0, :length 12.0}
{:kind :bad, :style "shortish", :width 5.0, :length 8.0})
user> (map instance-to-vector (dataset-seq ds))
([12.0 24.0 "longish" :good] [8.0 5.0 "shortish" :bad])
Filtering datasets
user> (use 'clj-ml.filters 'clj-ml.io)
nil
user> (def ds (load-instances :csv "file:///home/josh/git/clj-ml/iris.csv"))
#'user/ds
user> (def discretize (make-filter :unsupervised-discretize
{:dataset-format ds
:attributes [:sepallength :petallength]}))
#'user/discretize
user> (def filtered-ds (filter-apply discretize ds))
#'user/filtered-ds
user> (map instance-to-map (dataset-seq filtered-ds))
({:class :Iris-setosa, :petalwidth 0.2, :petallength :'(-inf-1.59]',
:sepalwidth 3.5, :sepallength :'(5.02-5.38]'}
{:class :Iris-setosa, :petalwidth 0.2, :petallength :'(-inf-1.59]',
:sepalwidth 3.0, :sepallength :'(4.66-5.02]'}
{:class :Iris-setosa, :petalwidth 0.2, :petallength :'(-inf-1.59]',
:sepalwidth 3.2, :sepallength :'(4.66-5.02]'}
{:class :Iris-setosa, :petalwidth 0.2, :petallength :'(-inf-1.59]',
:sepalwidth 3.1, :sepallength :'(-inf-4.66]'}
{:class :Iris-setosa, :petalwidth 0.2, :petallength :'(-inf-1.59]',
:sepalwidth 3.6, :sepallength :'(4.66-5.02]'}
...) ;; the petallength and sepallength attributes are now nominal
Equivalently,
user> (def filtered-ds (->> "file:///home/josh/git/clj-ml/iris.csv"
(load-instances :csv)
(make-apply-filter :unsupervised-discretize
{:attributes [:sepallength :petallength]})))
Using classifiers
user> (use 'clj-ml.classifiers 'clj-ml.data 'clj-ml.utils)
nil
user> (def ds (-> (load-instances :arff "file:///home/josh/git/clj-ml/iris.arff")
(dataset-set-class :class)))
#'user/ds
user> (def classifier (-> (make-classifier :decision-tree :c45)
(classifier-train ds)))
#'user/classifier
user> (def instance (-> (first (dataset-seq ds))
(instance-set-class-missing)))
user> (classifier-classify classifier instance)
:Iris-setosa
Evaluation:
user> (def evaluation (classifier-evaluate classifier :cross-validation ds 10))
#'user/evaluation
user> (clojure.pprint/pprint (dissoc evaluation :summary :confusion-matrix))
{:incorrect 7.0,
:root-relative-squared-error 36.693518966642074,
:sf-entropy-gain -4076.3670930399717,
:recall
{:Iris-setosa 0.9795918367346939,
:Iris-versicolor 0.94,
:Iris-virginica 0.94},
:kb-information 217.7935138195151,
:kb-relative-information 13741.240800360849,
:false-positive-rate
{:Iris-setosa 0.0,
:Iris-versicolor 0.04040404040404041,
:Iris-virginica 0.030303030303030304},
:percentage-correct 95.30201342281879,
:roc-area
{:Iris-setosa 0.984845423317842,
:Iris-versicolor 0.9456,
:Iris-virginica 0.9496},
:kb-mean-information 1.4617014350303028,
:percentage-unclassified 0.0,
:percentage-incorrect 4.697986577181208,
:root-mean-squared-error 0.17297908222448935,
:unclassified 0.0,
:correlation-coefficient
{:nan "Can't compute correlation coefficient: class is nominal!"},
:correct 142.0,
:sf-mean-entropy-gain -27.358168409664238,
:mean-absolute-error 0.04083212821368881,
:relative-absolute-error 9.187228848079984,
:error-rate 0.04697986577181208,
:kappa 0.9295222650179066,
:f-measure
{:Iris-setosa 0.9896907216494846,
:Iris-versicolor 0.9306930693069307,
:Iris-virginica 0.94},
:false-negative-rate
{:Iris-setosa 0.02040816326530612,
:Iris-versicolor 0.06,
:Iris-virginica 0.06},
:evaluation-object #<Evaluation weka.classifiers.Evaluation@6a7272ca>,
:average-cost 0.0,
:precision
{:Iris-setosa 1.0,
:Iris-versicolor 0.9215686274509803,
:Iris-virginica 0.94}}
user> (println (:summary evaluation))
Correctly Classified Instances 142 95.302 %
Incorrectly Classified Instances 7 4.698 %
Kappa statistic 0.9295
Mean absolute error 0.0408
Root mean squared error 0.173
Relative absolute error 9.1872 %
Root relative squared error 36.6935 %
Total Number of Instances 149
Ignored Class Unknown Instances 1
nil
user> (println (:confusion-matrix evaluation))
=== Confusion Matrix ===
a b c <-- classified as
48 1 0 | a = Iris-setosa
0 47 3 | b = Iris-versicolor
0 3 47 | c = Iris-virginica
nil
Saving and restoring (trained) classifiers:
user> (serialize-to-file classifier "my-classifier.bin")
"my-classifier.bin"
user> (def classifier2 (deserialize-from-file "my-classifier.bin"))
#'user/classifier2
user> (classifier-classify classifier2 instance)
:Iris-setosa
Text document handling:
user> (def docs [{:id 10
:title "Document title 1"
:fulltext "This is the fulltext..."
:has-class? false}
{:id 11
:title "Another document title"
:fulltext "Some more \"fulltext\"; rabbit artificial machine bananas"
:has-class? true}])
#'user/docs
user> (docs-to-dataset docs "bananas-model" "my-models" :stemmer true :lowercase false)
#<Instances @relation 'docs-weka.filters.unsupervised.attribute.StringToWordVector...'
@attribute class {no,yes}
@attribute title-1 numeric
@attribute title-Another numeric
@attribute title-Document numeric
@attribute title-document numeric
@attribute title-titl numeric
@attribute fulltext-Some numeric
@attribute fulltext-This numeric
@attribute fulltext-artifici numeric
@attribute fulltext-banana numeric
@attribute fulltext-fulltext numeric
@attribute fulltext-is numeric
@attribute fulltext-machin numeric
@attribute fulltext-more numeric
@attribute fulltext-rabbit numeric
@attribute fulltext-the numeric
@data
{0 yes,1 0.480453,3 0.480453,7 0.480453,11 0.480453,15 0.480453}
{2 0.480453,4 0.480453,6 0.480453,8 0.480453,9 0.480453,12 0.480453,13 0.480453,14 0.480453}>
user>
Words appearing in the dataset will only be those appearing in thedocuments (or a subset; by default, the most common 1000 words). Thispresents a problem when new documents are loaded and used in aclassifier trained on other documents. The classifier will not knowhow to handle word attributes that were not present in the trainingset.
The docs-to-dataset function provides the ability to save thetraining documents dataset and "filter" the testing documents throughthis dataset to ensure the same word attributes are extracted for bothsets. The following example shows that the words "foo, bar, baz, quux"are ignored in the new (testing) documents, and all the originalattributes in the training dataset are retained.
user> (docs-to-dataset docs "Topic" "Sports" 1 "/tmp"
:stemmer true :lowercase false :training true)
#<Instances @relation 'docs-weka.filters.unsupervised.attribute.StringToWordVector...'
@attribute class {no,yes}
@attribute title-1 numeric
@attribute title-Another numeric
@attribute title-Document numeric
@attribute title-document numeric
@attribute title-titl numeric
@attribute fulltext-Some numeric
@attribute fulltext-This numeric
@attribute fulltext-artifici numeric
@attribute fulltext-banana numeric
@attribute fulltext-fulltext numeric
@attribute fulltext-is numeric
@attribute fulltext-machin numeric
@attribute fulltext-more numeric
@attribute fulltext-rabbit numeric
@attribute fulltext-the numeric
@data
{2 0.480453,4 0.480453,6 0.480453,8 0.480453,9 0.480453,12 0.480453,13 0.480453,14 0.480453}
{0 yes,1 0.480453,3 0.480453,7 0.480453,11 0.480453,15 0.480453}>
user> (def docs2 [{:title "Document title 1 foo bar"
:fulltext "baz rabbit quux"
:terms {"Topic" ["Sports"]}}])
#'user/docs2
user> (docs-to-dataset docs2 "Topic" "Sports" 1 "/tmp"
:stemmer true :lowercase false :testing true)
#<Instances @relation 'docs-weka.filters.unsupervised.attribute.StringToWordVector...'
@attribute class {no,yes}
@attribute title-1 numeric
@attribute title-Another numeric
@attribute title-Document numeric
@attribute title-document numeric
@attribute title-titl numeric
@attribute fulltext-Some numeric
@attribute fulltext-This numeric
@attribute fulltext-artifici numeric
@attribute fulltext-banana numeric
@attribute fulltext-fulltext numeric
@attribute fulltext-is numeric
@attribute fulltext-machin numeric
@attribute fulltext-more numeric
@attribute fulltext-rabbit numeric
@attribute fulltext-the numeric
@data
{0 yes,1 0.480453,3 0.480453,14 0.480453}>
user>
Using clusterers
user> (use 'clj-ml.clusterers)
nil
user> (def ds (-> (load-instances :arff "file:///home/josh/git/clj-ml/iris.arff")
(dataset-remove-attribute-at 4)))
#'user/ds
user> ds
#<Instances @relation iris
@attribute sepallength numeric
@attribute sepalwidth numeric
@attribute petallength numeric
@attribute petalwidth numeric
@data
5.1,3.5,1.4,0.2
4.9,3,1.4,0.2
4.7,3.2,1.3,0.2
4.6,3.1,1.5,0.2
5,3.6,1.4,0.2
5.4,3.9,1.7,0.4
4.6,3.4,1.4,0.3
...
user> (def clusterer (make-clusterer :k-means {:number-clusters 3}))
#'user/clusterer
user> (clusterer-build clusterer ds)
nil
user> clusterer
#<SimpleKMeans
kMeans
======
Number of iterations: 6
Within cluster sum of squared errors: 6.998114004826762
Missing values globally replaced with mean/mode
Cluster centroids:
Cluster#
Attribute Full Data 0 1 2
(150) (61) (50) (39)
=========================================================
sepallength 5.8433 5.8885 5.006 6.8462
sepalwidth 3.054 2.7377 3.418 3.0821
petallength 3.7587 4.3967 1.464 5.7026
petalwidth 1.1987 1.418 0.244 2.0795
>
user> (def clustered-ds (clusterer-cluster clusterer ds))
#'user/clustered-ds
user> clustered-ds
#<ClojureInstances @relation 'clustered iris'
@attribute sepallength numeric
@attribute sepalwidth numeric
@attribute petallength numeric
@attribute petalwidth numeric
@attribute class {0,1,2}
@data
5.1,3.5,1.4,0.2,1
4.9,3,1.4,0.2,1
4.7,3.2,1.3,0.2,1
4.6,3.1,1.5,0.2,1
5,3.6,1.4,0.2,1
5.4,3.9,1.7,0.4,1
4.6,3.4,1.4,0.3,1
5,3.4,1.5,0.2,1
4.4,2.9,1.4,0.2,1
...
Example: Home price prediction
http://www.ibm.com/developerworks/library/os-weka1/
user> (def homes (make-dataset "homes" [:house-size :lot-size :bedrooms
:granite :bathroom :sellingPrice]
[[3529, 9191, 6, 0, 0, 205000]
[3247, 10061, 5, 1, 1, 224900]
[4032, 10150, 5, 0, 1, 197900]
[2397, 14156, 4, 1, 0,189900]
[2200, 9600, 4, 0, 1, 195000]
[3536, 19994, 6, 1, 1,325000]
[2983, 9365, 5, 0, 1, 230000]]))
#'user/homes
user> (def homes (dataset-set-class homes :sellingPrice))
#'user/homes
user> homes
#<ClojureInstances @relation homes
@attribute house-size numeric
@attribute lot-size numeric
@attribute bedrooms numeric
@attribute granite numeric
@attribute bathroom numeric
@attribute sellingPrice numeric
@data
3529,9191,6,0,0,205000
3247,10061,5,1,1,224900
4032,10150,5,0,1,197900
2397,14156,4,1,0,189900
2200,9600,4,0,1,195000
3536,19994,6,1,1,325000
2983,9365,5,0,1,230000>
user> (def reg (classifier-train (make-classifier :regression :linear) homes))
#'user/reg
user> reg
#<LinearRegression
Linear Regression Model
sellingPrice =
-26.6882 * house-size +
7.0551 * lot-size +
43166.0767 * bedrooms +
42292.0901 * bathroom +
-21661.1208>
user>
user> (classifier-predict-numeric reg (make-instance homes [3198, 9669, 5, 1, 1, nil]))
219328.35717359098
Example: Predicting survival on the Titanic
https://www.kaggle.com/c/titanic-gettingStarted
First globally replace all double quoted strings ""foo"" withbackslash quoted strings: \"foo\". Weka does not handle the former.
user> (require '[clj-ml.io :refer [load-instances]]
'[clj-ml.data :refer [dataset-set-class dataset-class-index dataset-class-name]]
'[clj-ml.filters :refer [make-apply-filter]]
'[clj-ml.classifiers :refer [classifier-evaluate make-classifier]])
nil
user> (def titanicds (load-instances :csv "file:///home/josh/git/clj-ml/titanic-train.csv"))
user> titanicds
#<Instances @relation stream
@attribute PassengerId numeric
@attribute Survived numeric
@attribute Pclass numeric
@attribute Name {'Braund, Mr. Owen Harris','Cumings, Mrs. John Bradley (Florence Briggs Thayer)', ...}
@attribute Sex {male,female}
@attribute Age numeric
@attribute SibSp numeric
@attribute Parch numeric
@attribute Ticket {'A/5 21171','PC 17599','STON/O2. 3101282',113803.0, ...}
@attribute Fare numeric
@attribute Cabin {C85,C123,E46,G6,C103,D56,A6,'C23 C25 C27', ...}
@attribute Embarked {S,C,Q}
@data
1,0,3,'Braund, Mr. Owen Harris',male,22,1,0,'A/5 21171',7.25,?,S
2,1,1,'Cumings, Mrs. John Bradley (Florence Briggs Thayer)',female,38,1,0,'PC 17599',71.2833,C85,C
3,1,3,'Heikkinen, Miss. Laina',female,26,0,0,'STON/O2. 3101282',7.925,?,S
4,1,1,'Futrelle, Mrs. Jacques Heath (Lily May Peel)',female,35,1,0,113803.0,53.1,C123,S
5,0,3,'Allen, Mr. William Henry',male,35,0,0,373450.0,8.05,?,S
6,0,3,'Moran, Mr. James',male,?,0,0,330877.0,8.4583,?,Q
7,0,1,'McCarthy, Mr. Timothy J',male,54,0,0,17463.0,51.8625,E46,S
8,0,3,'Palsson, Master. Gosta Leonard',male,2,3,1,349909.0,21.075,?,S
9,1,3,'Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)',female,27,0,2,347742.0,11.1333,?,S
10,1,2,'Nasser, Mrs. Nicholas (Adele Achem)',female,14,1,0,237736.0,30.0708,?,C
11,1,3,'Sandstrom, Miss. Marguerite Rut',female,4,1,1,'PP 9549',16.7,G6,S
...
>
#'user/titanicds
user> (def titanicds (dataset-set-class titanicds :Survived))
#'user/titanicds
user> (dataset-class-index titanicds)
1
user> (def titanicds (make-apply-filter :numeric-to-nominal
{:attributes [:Survived]}
titanicds))
#'user/titanicds
user> titanicds
#<Instances @relation stream-weka.filters.unsupervised.attribute.NumericToNominal-R2
@attribute PassengerId numeric
@attribute Survived {0,1}
@attribute Pclass numeric
...
>
user> (def titanicds (make-apply-filter :replace-missing-values {} titanicds))
user> (def titanicds (make-apply-filter :remove-attributes
{:attributes [:PassengerId :Name :Ticket :Cabin]}
titanicds))
#'user/titanicds
user> titanicds
#<Instances @relation 'stream-weka.filters.unsupervised.attribute.NumericToNominal...'
@attribute Survived {0,1}
@attribute Pclass numeric
@attribute Sex {male,female}
@attribute Age numeric
@attribute SibSp numeric
@attribute Parch numeric
@attribute Fare numeric
@attribute Embarked {S,C,Q}
@data
0,3,male,22,1,0,7.25,S
1,1,female,38,1,0,71.2833,C
1,3,female,26,0,0,7.925,S
1,1,female,35,1,0,53.1,S
0,3,male,35,0,0,8.05,S
0,3,male,?,0,0,8.4583,Q
...
>
user> (dataset-class-index titanicds)
0
user> (dataset-class-name titanicds)
:Survived
user> (def evaluation (classifier-evaluate (make-classifier :decision-tree :random-forest)
:cross-validation titanicds 10))
#'user/evaluation
user> (println (:summary evaluation))
Correctly Classified Instances 727 81.5937 %
Incorrectly Classified Instances 164 18.4063 %
Kappa statistic 0.6039
Mean absolute error 0.2409
Root mean squared error 0.3819
Relative absolute error 50.9302 %
Root relative squared error 78.532 %
Total Number of Instances 891
nil
user> (println (:confusion-matrix evaluation))
=== Confusion Matrix ===
a b <-- classified as
483 66 | a = 0
98 244 | b = 1
nil
Ok, looks good, let's try training on the full training data andtesting on the testing data.
user> (require '[clj-ml.data :refer [dataset-as-maps dataset-seq]]
'[clj-ml.classifiers :refer [classifier-train classifier-classify]])
user> (def titanic-testds (load-instances :csv "file:///home/josh/git/clj-ml/titanic-test.csv"))
nil
user> (def titanic-test-passids (map (comp int :PassengerId)
(dataset-as-maps titanic-testds)))
#'user/titanic-test-passids
user> titanic-test-passids
(892 893 894 895 896 897 898 899 900 ...)
user> (def titanic-testds (->> titanic-testds
(make-apply-filter :remove-attributes
{:attributes [:PassengerId :Name :Ticket :Cabin]})
(make-apply-filter :replace-missing-values {})
(make-apply-filter :add-attribute
{:type :nominal :name :Survived
:column 0 :labels ["0" "1"]})))
#'user/titanic-testds
user> (def titanic-testds (dataset-set-class titanic-testds :Survived))
#'user/titanic-testds
user> titanic-testds
#<Instances @relation 'stream-weka.filters.unsupervised.attribute.Remove...'
@attribute Survived {0,1}
@attribute Pclass numeric
@attribute Sex {male,female}
@attribute Age numeric
@attribute SibSp numeric
@attribute Parch numeric
@attribute Fare numeric
@attribute Embarked {Q,S,C}
@data
?,3,male,34.5,0,0,7.8292,Q
?,3,female,47,1,0,7,S
?,2,male,62,0,0,9.6875,Q
?,3,matitanle,27,0,0,8.6625,S
?,3,female,22,1,1,12.2875,S
?,3,male,14,0,0,9.225,S
?,3,female,30,0,0,7.6292,Q
...
>
user> (def classifier (classifier-train (make-classifier :decision-tree :random-forest) titanicds))
#'user/classifier
user> (def preds (for [instance (dataset-seq titanic-testds)]
(name (classifier-classify classifier instance))))
#'user/preds
user> preds
("0" "1" "0" "0" "0" "0" "1" "0" "0" "0" ...)
#'user/preds
user> (spit "titanic-predictions.csv"
(clojure.string/join "\n" (cons "Survived,PassengerId"
(map (fn [c1 c2] (format "%s,%d" c1 c2))
preds titanic-test-passids))))
nil
user> (println (slurp "titanic-predictions.csv"))
Survived,PassengerId
0,892
1,893
0,894
0,895
0,896
0,897
1,898
0,899
0,900
0,901
0,902
...
How to add a Weka classifier
- In
classifiers.clj:- Add the appropriate import to the top of the file.
- Create another implementation of
make-classifier-options(usingdefmethod, like the others). At this point, you must decide the pair of keywords that identify your algorithm, such as:decision-tree :c45. List all the Weka options that the classifier accepts. Usecheck-optionsfor options that are either present or absent, andcheck-option-valuesfor options that require a value in addition to the option. - Add documentation to the
(defmulti make-classifier ...)docstring. - Create another implementation of
make-classifier(usingdefmethod, like the others).
- Ideally, add some test cases in
classifers_test.clj.
Thanks YourKit!
YourKit is kindly supporting open source projects with itsfull-featured Java Profiler. YourKit, LLC is the creator ofinnovative and intelligent tools for profiling Java and .NETapplications. Take a look at YourKit's leading software products: YourKit JavaProfiler and YourKit .NETProfiler.
License
MIT License
Authors
- 2010: Antonio Garrote
- 2010-2013: Ben Mabey
- 2013: Joshua Eckroth
-
1.definition of ML the design and development of algorithms that allow computers to evolve behaviors based on empirical data 2.application computer vision(数码相机中的人脸识别)、speech recognition(科大讯飞、百度语音输入法)
-
http://m.sciencemag.org/site/feature/data/compsci/machine_learning.xhtml
-
朴素贝叶斯 参考[1] 事件A和B同时发生的概率为在A发生的情况下发生B或者在B发生的情况下发生A P(A∩B)=P(A)∗P(B|A)=P(B)∗P(A|B) 所以有： P(A|B)=P(B|A)∗P(A)P(B) 对于给出的待分类项，求解在此项出现的条件下各个目标类别出现的概率，哪个最大，就认为此待分类项属于哪个类别 工作原理 1、假设现在有样本x=(a1,a2,a3,…an)这个待分类项(并
-
from: https://sgfin.github.io/learning-resources/ This is a not-particularly-systematic attempt to curate a handful of my favorite resources for learning statistics and machine learning. This isn’t me
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