ugtm 2.0.0

sklearn集成

ugtm v2.0提供sklearn兼容的gtm转换器（egtm）、gtm分类器（egtc）和gtm回归器（egtr）：

from ugtm import eGTM, eGTC, eGTR
import numpy as np

# Dummy train and test
X_train = np.random.randn(100, 50)
X_test = np.random.randn(50, 50)
y_train = np.random.choice([1, 2, 3], size=100)

# GTM transformer
transformed = eGTM().fit(X_train).transform(X_test)

# Predict new labels using GTM classifier (GTC)
predicted_labels = eGTC().fit(X_train, y_train).predict(X_test)

# Predict new continuous outcomes using GTM regressor (GTR)
predicted_labels = eGTR().fit(X_train, y_train).predict(X_test)

以下部分将显示sklearn框架中未定义的函数。

基本功能

ugtm提供了gtm（生成地形图）、kgtm（核心生成地形图）、gtm分类模型（knn、bayes）和gtm回归模型的实现。ugtm还实现了交叉验证选项，可用于比较gtm分类模型和支持向量机分类模型，以及gtm回归模型和支持向量机回归模型。典型用法：

#!/usr/bin/env python

import ugtm
import numpy as np

#generate sample data and labels: replace this with your own data
data=np.random.randn(100,50)
labels=np.random.choice([1,2],size=100)

#build GTM map
gtm=ugtm.runGTM(data=data,verbose=True)

#plot GTM map (html)
gtm.plot_html(output="out")

有关安装说明，请参阅https://github.com/hagax8/ugtm

构建和绘制GTM地图（或KGTM地图）

gtm对象可以通过在数据集上运行rungtm函数来创建。rungtm的参数为：k=sqrt（节点数），m=sqrt（rbf中心数），s=rbf宽度因子，regul=正则化系数。默认情况下，期望最大化算法的迭代次数设置为200。这是一个随机数据示例：

import ugtm

#import numpy to generate random data
import numpy as np

#generate random data (independent variables x),
#discrete labels (dependent variable y),
#and continuous labels (dependent variable y),
#to experiment with categorical or continuous outcomes

train = np.random.randn(20,10)
test = np.random.randn(20,10)
labels=np.random.choice(["class1","class2"],size=20)
activity=np.random.randn(20,1)

#create a gtm object and write model
gtm = ugtm.runGTM(train)
gtm.write("testout1")

#run verbose
gtm = ugtm.runGTM(train, verbose=True)

#to run a kernel GTM model instead, run following:
gtm = ugtm.runkGTM(train, doKernel=True, kernel="linear")

#access coordinates (means or modes), and responsibilities of gtm object
gtm_coordinates = gtm.matMeans
gtm_modes = gtm.matModes
gtm_responsibilities = gtm.matR

绘制HTML地图

在gtm对象上调用plot_html（）函数：

#run model on train
gtm = ugtm.runGTM(train)

# ex. plot gtm object with landscape, html: labels are continuous
gtm.plot_html(output="testout10",labels=activity,discrete=False,pointsize=20)

# ex. plot gtm object with landscape, html: labels are discrete
gtm.plot_html(output="testout11",labels=labels,discrete=True,pointsize=20)

# ex. plot gtm object with landscape, html: labels are continuous
# no interpolation between nodes
gtm.plot_html(output="testout12",labels=activity,discrete=False,pointsize=20, \
              do_interpolate=False,ids=labels)

# ex. plot gtm object with landscape, html: labels are discrete,
# no interpolation between nodes
gtm.plot_html(output="testout13",labels=labels,discrete=True,pointsize=20, \
              do_interpolate=False)

绘制pdf地图

对gtm对象调用plot（）函数：

#run model on train
gtm = ugtm.runGTM(train)

# ex. plot gtm object, pdf: no labels
gtm.plot(output="testout6",pointsize=20)

# ex. plot gtm object with landscape, pdf: labels are discrete
gtm.plot(output="testout7",labels=labels,discrete=True,pointsize=20)

# ex. plot gtm object with landscape, pdf: labels are continuous
gtm.plot(output="testout8",labels=activity,discrete=False,pointsize=20)

绘制多面板视图

对gtm对象调用plot_multipanel（）函数。 这将绘制一个通用模型视图，显示有或无点的方式、模式、景观。 plot_multipanel函数仅在定义了标签时才起作用：

#run model on train
gtm = ugtm.runGTM(train)

# ex. with discrete labels and inter-node interpolation
gtm.plot_multipanel(output="testout2",labels=labels,discrete=True,pointsize=20)

# ex. with continuous labels and inter-node interpolation
gtm.plot_multipanel(output="testout3",labels=activity,discrete=False,pointsize=20)

# ex. with discrete labels and no inter-node interpolation
gtm.plot_multipanel(output="testout4",labels=labels,discrete=True,pointsize=20, \
                    do_interpolate=False)

# ex. with continuous labels and no inter-node interpolation
gtm.plot_multipanel(output="testout5",labels=activity,discrete=False,pointsize=20, \
                    do_interpolate=False)

使用transform（）函数可以将新数据投影到gtm映射上，该函数将gtm模型、训练集和测试集作为输入。然后，列车组仅用于对基于列车的测试集执行数据预处理（例如：在运行算法之前，对列车和测试集应用相同的PCA转换）：

#run model on train
gtm = ugtm.runGTM(train,doPCA=True)

#test new data (test)
transformed=ugtm.transform(optimizedModel=gtm,train=train,test=test,doPCA=True)

#plot transformed test (html)
transformed.plot_html(output="testout14",pointsize=20)

#plot transformed test (pdf)
transformed.plot(output="testout15",pointsize=20)

#plot transformed data on existing classification model,
#using training set labels
gtm.plot_html_projection(output="testout16",projections=transformed,\
                         labels=labels, \
                         discrete=True,pointsize=20)