Smile
=====
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Smile (Statistical Machine Intelligence and Learning Engine) is
a fast and comprehensive machine learning, NLP, linear algebra,
graph, interpolation, and visualization system in Java and Scala.
With advanced data structures and algorithms,
Smile delivers state-of-art performance.
Smile covers every aspect of machine learning, including classification,
regression, clustering, association rule mining, feature selection,
manifold learning, multidimensional scaling, genetic algorithms,
missing value imputation, efficient nearest neighbor search, etc.
Smile is well documented and please check out the
[project website](http://haifengl.github.io/smile/)
for programming guides and more information.
You can use the libraries through Maven central repository by adding the following to your project pom.xml file.
```
<dependency>
<groupId>com.github.haifengl</groupId>
<artifactId>smile-core</artifactId>
<version>1.5.2</version>
</dependency>
```
For NLP, use the artifactId smile-nlp.
For Scala API, please use
```
libraryDependencies += "com.github.haifengl" %% "smile-scala" % "1.5.2"
```
To enable machine optimized matrix computation, the users should add
the dependency of smile-netlib:
```
<dependency>
<groupId>com.github.haifengl</groupId>
<artifactId>smile-netlib</artifactId>
<version>1.5.2</version>
</dependency>
```
and also make their machine-optimized libblas3 (CBLAS) and liblapack3 (Fortran)
available as shared libraries at runtime. This module employs the highly efficient
[netlib-java](https://github.com/fommil/netlib-java#netlib-java) library.
OS X
----
Apple OS X requires no further setup as it ships with the veclib framework.
Linux
-----
Generically-tuned ATLAS and OpenBLAS are available with most distributions
and must be enabled explicitly using the package-manager. For example,
- sudo apt-get install libatlas3-base libopenblas-base
- sudo update-alternatives --config libblas.so
- sudo update-alternatives --config libblas.so.3
- sudo update-alternatives --config liblapack.so
- sudo update-alternatives --config liblapack.so.3
However, these are only generic pre-tuned builds. If you have an Intel MKL licence,
you could also create symbolic links from libblas.so.3 and liblapack.so.3 to libmkl_rt.so
or use Debian's alternatives system.
Windows
-------
The native_system builds expect to find libblas3.dll and liblapack3.dll
on the %PATH% (or current working directory). Smile ships a prebuilt
[OpenBLAS](http://www.openblas.net/).
The users can also install vendor-supplied implementations, which may
offer better performance.
Smile comes with an interactive shell. Download pre-packaged Smile from the [releases page](https://github.com/haifengl/smile/releases).
In the home directory of Smile, type
```
./bin/smile
```
to enter the shell, which is based on Scala interpreter. So you can run any valid Scala expressions in the shell.
In the simplest case, you can use it as a calculator. Besides, all high-level Smile operators are predefined
in the shell. By default, the shell uses up to 4GB memory. If you need more memory to handle large data,
use the option `-J-Xmx`. For example,
```
./bin/smile -J-Xmx8192M
```
You can also modify the configuration file `./conf/application.ini` for the memory and other JVM settings.
For detailed help, checkout the [project website](http://haifengl.github.io/smile/).
Smile implements the following major machine learning algorithms:
* **Classification**
Support Vector Machines, Decision Trees, AdaBoost, Gradient Boosting, Random Forest, Logistic Regression, Neural Networks, RBF Networks, Maximum Entropy Classifier, KNN, Naïve Bayesian, Fisher/Linear/Quadratic/Regularized Discriminant Analysis.
* **Regression**
Support Vector Regression, Gaussian Process, Regression Trees, Gradient Boosting, Random Forest, RBF Networks, OLS, LASSO, Ridge Regression.
* **Feature Selection**
Genetic Algorithm based Feature Selection, Ensemble Learning based Feature Selection, Signal Noise ratio, Sum Squares ratio.
* **Clustering**
BIRCH, CLARANS, DBSCAN, DENCLUE, Deterministic Annealing, K-Means, X-Means, G-Means, Neural Gas, Growing Neural Gas, Hierarchical Clustering, Sequential Information Bottleneck, Self-Organizing Maps, Spectral Clustering, Minimum Entropy Clustering.
* **Association Rule & Frequent Itemset Mining**
FP-growth mining algorithm
* **Manifold learning**
IsoMap, LLE, Laplacian Eigenmap, t-SNE, PCA, Kernel PCA, Probabilistic PCA, GHA, Random Projection
* **Multi-Dimensional Scaling**
Classical MDS, Isotonic MDS, Sammon Mapping
* **Nearest Neighbor Search**
BK-Tree, Cover Tree, KD-Tree, LSH
* **Sequence Learning**
Hidden Markov Model, Conditional Random Field.
* **Natural Language Processing**
Sentence Splitter and Tokenizer, Bigram Statistical Test, Phrase Extractor, Keyword Extractor, Stemmer, POS Tagging, Relevance Ranking
Model Serialization
===================
Most models support the Java `Serializable` interface (all classifiers do support `Serializable` interface) so that
you can use them in Spark. For reading/writing the models in non-Java code, we suggest [XStream](https://github.com/x-stream/xstream) to serialize the trained models.
XStream is a simple library to serialize objects to XML and back again. XStream is easy to use and doesn't require mappings
(actually requires no modifications to objects). [Protostuff](http://code.google.com/p/protostuff/) is a
nice alternative that supports forward-backward compatibility (schema evolution) and validation.
Beyond XML, Protostuff supports many other formats such as JSON, YAML, protobuf, etc. For some predictive models,
we look forward to supporting PMML (Predictive Model Markup Language), an XML-based file format developed by the Data Mining Group.
Smile Scala API provides `read()`, `read.xstream()`, `write()`, and `write.xstream()` functions in package smile.io.
SmilePlot
=========
Smile also has a Swing-based data visualization library SmilePlot, which provides scatter plot, line plot, staircase plot, bar plot, box plot, histogram, 3D histogram, dendrogram, heatmap, hexmap, QQ plot, contour plot, surface, and wireframe. The class PlotCanvas provides builtin functions such as zoom in/out, export, print, customization, etc.
SmilePlot requires SwingX library for JXTable. But if your environment cannot use SwingX, it is easy to remove this dependency by using JTable.
To use SmilePlot, add the following to dependencies
```
<dependency>
<groupId>com.github.haifengl</groupId>
<artifactId>smile-plot</artifactId>
<version>1.5.2</version>
</dependency>
```
Demo Gallery
============
<table class="center" width="100%">
<tr>
<td width="50%">
<figure>
<a href="http://haifengl.github.io/smile/gallery/smile-demo-kpca.png"><img src="http://haifengl.github.io/smile/gallery/smile-demo-kpca-small.png" alt="Kernel PCA"></a>
<figcaption><h2>Kernel PCA</h2></figcaption>
</figure>
</td>
<td width="50%">
<figure>
<a href="http://haifengl.github.io/smile/gallery/smile-demo-isomap.png"><img src="http://haifengl.github.io/smile/gallery/smile-demo-isomap-small.png" alt="IsoMap"></a>
<figcaption><h2>IsoMap</h2></figcaption>
</figure>
</td>
</tr>
<tr>
<td width="50%">
<figure>
<a href="http://haifengl.github.io/smile/gallery/smile-demo-mds.png"><img src="http://haifengl.github.io/smile/gal
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matlab做TSNE的详细代码-smile:微笑
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matlab做T SNE的详细代码微笑 Smile(统计机器智能和学习引擎)是Java和Scala中快速而全面的机器学习,NLP,线性代数,图形,插值和可视化系统。 凭借先进的数据结构和算法,Smile可提供最先进的性能。 Smile涵盖了机器学习的各个方面,包括分类,回归,聚类,关联规则挖掘,特征选择,流形学习,多维缩放,遗传算法,缺失值插补,有效的最近邻搜索等。 Smile有充分的文献记录,请查阅的编程指南和更多信息。 通过将以下内容添加到项目pom.xml文件中,可以通过Maven中央存储库使用这些库。 <dependency> <groupId>com.github.haifengl</groupId> <artifactId>smile-core</artifactId> <version>1.5.2</version> </dependency> 对于NLP,请使用artifactIdId smile-nlp。 对于Scala API,请使用 libraryDependencies += "com.github.haifengl" %% "smile-scala" % "1
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matlab做TSNE的详细代码-smile:微笑 (1312个子文件)
plot.tok.gt9.5000 654KB
quote.tok.gt9.5000 616KB
house_16H.arff 4.19MB
mv.arff 3.57MB
ailerons.arff 3.57MB
puma32H.arff 2.68MB
fried.arff 2.6MB
bank32nh.arff 2.39MB
house_8L.arff 2.27MB
elevators.arff 2.1MB
cal_housing.arff 1.98MB
pol.arff 1.65MB
2dplanes.arff 1.25MB
kin8nm.arff 1.08MB
cpu_act.arff 1.01MB
puma8NH.arff 685KB
bank8FM.arff 674KB
cpu_small.arff 567KB
delta_elevators.arff 354KB
delta_ailerons.arff 302KB
soybean.arff 198KB
segment-challenge.arff 196KB
abalone.arff 193KB
segment-test.arff 107KB
triazines.arff 80KB
stock.arff 56KB
wisconsin.arff 48KB
housing.arff 36KB
auto_price.arff 16KB
pyrim.arff 16KB
autoMpg.arff 13KB
labor.arff 8KB
iris.arff 7KB
cpu.with.vendor.arff 7KB
machine_cpu.arff 6KB
cpu.arff 5KB
servo.arff 5KB
contact-lenses.arff 3KB
diabetes_numeric.arff 1KB
weather.nominal.arff 587B
weather.arff 489B
string.arff 330B
sparse.arff 213B
date.arff 123B
pima.D38.N768.C2 18KB
smile.conf 0B
cerulean.min.css 125KB
custom.css 8KB
pager.css 593B
toc.css 66B
train-1m.csv 46.62MB
train-0.1m.csv 4.66MB
test.csv 4.66MB
diabetes.csv 545KB
prostate-train.csv 4KB
prostate-test.csv 2KB
gdp.csv 2KB
kosarak.dat 30.55MB
news20.dat 8.59MB
news20.t.dat 2.17MB
COMBO17.dat 543KB
synthetic_control.data 282KB
movement_libras.data 250KB
abalone-train.data 141KB
abalone-test.data 47KB
libblas3.dll 36.56MB
liblapack3.dll 36.56MB
libgfortran-3.dll 1.22MB
libquadmath-0.dll 324KB
libgcc_s_seh-1.dll 81KB
allaml.dataset.gct 3.89MB
.gitignore 343B
5by5_rua.hb 520B
msr_paraphrase_README.htm 70KB
Microsoft Shared Source License.htm 10KB
jupyter-notebook.html 293KB
classification-content.html 58KB
regression-content.html 43KB
clustering-content.html 41KB
nlp-content.html 35KB
data-content.html 34KB
feature-content.html 26KB
validation-content.html 24KB
index-content.html 23KB
visualization-content.html 21KB
vector-quantization-content.html 17KB
statistics-content.html 14KB
overview-content.html 13KB
manifold-content.html 13KB
association-rule-content.html 12KB
missing-value-imputation-content.html 11KB
linear-algebra-content.html 11KB
wavelet-content.html 11KB
interpolation-content.html 10KB
mds-content.html 10KB
faq-content.html 9KB
index.html 8KB
shell-content.html 8KB
validation.html 8KB
vector-quantization.html 8KB
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