Some ml algorithms in python and R

ml algorithms/AlgorithmsInPython.py

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+#Linear Regression
+
+#Import Library
+#Import other necessary libraries like pandas, numpy...
+from sklearn import linear_model
+#Load Train and Test datasets
+#Identify feature and response variable(s) and values must be numeric and numpy arrays
+x_train=input_variables_values_training_datasets
+y_train=target_variables_values_training_datasets
+x_test=input_variables_values_test_datasets
+# Create linear regression object
+linear = linear_model.LinearRegression()
+# Train the model using the training sets and check score
+linear.fit(x_train, y_train)
+linear.score(x_train, y_train)
+#Equation coefficient and Intercept
+print('Coefficient: \n', linear.coef_)
+print('Intercept: \n', linear.intercept_)
+#Predict Output
+predicted= linear.predict(x_test)
+
+
+
+#Logistic Regression
+
+
+#Import Library
+from sklearn.linear_model import LogisticRegression
+#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
+# Create logistic regression object
+model = LogisticRegression()
+# Train the model using the training sets and check score
+model.fit(X, y)
+model.score(X, y)
+#Equation coefficient and Intercept
+print('Coefficient: \n', model.coef_)
+print('Intercept: \n', model.intercept_)
+#Predict Output
+predicted= model.predict(x_test)
+
+
+
+
+#Decision Tree
+#Import Library
+#Import other necessary libraries like pandas, numpy...
+from sklearn import tree
+#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
+# Create tree object
+model = tree.DecisionTreeClassifier(criterion='gini') # for classification, here you can change the algorithm as gini or entropy (information gain) by default it is gini
+# model = tree.DecisionTreeRegressor() for regression
+# Train the model using the training sets and check score
+model.fit(X, y)
+model.score(X, y)
+#Predict Output
+predicted= model.predict(x_test)
+
+
+#SVM
+
+#Import Library
+from sklearn import svm
+#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
+# Create SVM classification object
+model = svm.svc() # there is various option associated with it, this is simple for classification. You can refer link, for mo# re detail.
+# Train the model using the training sets and check score
+model.fit(X, y)
+model.score(X, y)
+#Predict Output
+predicted= model.predict(x_test)
+
+# Naive Bayes
+
+#Import Library
+from sklearn.naive_bayes import GaussianNB
+#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
+# Create SVM classification object model = GaussianNB() # there is other distribution for multinomial classes like Bernoulli Naive Bayes, Refer link
+# Train the model using the training sets and check score
+model.fit(X, y)
+#Predict Output
+predicted= model.predict(x_test)
+
+#kNN (k- Nearest Neighbors)
+
+#Import Library
+from sklearn.neighbors import KNeighborsClassifier
+#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
+# Create KNeighbors classifier object model
+KNeighborsClassifier(n_neighbors=6) # default value for n_neighbors is 5
+# Train the model using the training sets and check score
+model.fit(X, y)
+#Predict Output
+predicted= model.predict(x_test)
+
+#K-Means
+#Import Library
+from sklearn.cluster import KMeans
+#Assumed you have, X (attributes) for training data set and x_test(attributes) of test_dataset
+# Create KNeighbors classifier object model
+k_means = KMeans(n_clusters=3, random_state=0)
+# Train the model using the training sets and check score
+model.fit(X)
+#Predict Output
+predicted= model.predict(x_test)
+
+#Random Forest
+
+#Import Library
+from sklearn.ensemble import RandomForestClassifier
+#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
+# Create Random Forest object
+model= RandomForestClassifier()
+# Train the model using the training sets and check score
+model.fit(X, y)
+#Predict Output
+predicted= model.predict(x_test)
+
+#Dimensionality Reduction Algorithms
+
+#Import Library
+from sklearn import decomposition
+#Assumed you have training and test data set as train and test
+# Create PCA obeject pca= decomposition.PCA(n_components=k) #default value of k =min(n_sample, n_features)
+# For Factor analysis
+#fa= decomposition.FactorAnalysis()
+# Reduced the dimension of training dataset using PCA
+train_reduced = pca.fit_transform(train)
+#Reduced the dimension of test dataset
+test_reduced = pca.transform(test)

ml algorithms/AlgorithmsInR.r

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+#Linear Regression
+
+#Load Train and Test datasets
+#Identify feature and response variable(s) and values must be numeric and numpy arrays
+x_train <- input_variables_values_training_datasets
+y_train <- target_variables_values_training_datasets
+x_test <- input_variables_values_test_datasets
+x <- cbind(x_train,y_train)
+# Train the model using the training sets and check score
+linear <- lm(y_train ~ ., data = x)
+summary(linear)
+#Predict Output
+predicted= predict(linear,x_test)
+
+#Logistic Regression
+
+x <- cbind(x_train,y_train)
+# Train the model using the training sets and check score
+logistic <- glm(y_train ~ ., data = x,family='binomial')
+summary(logistic)
+#Predict Output
+predicted= predict(logistic,x_test)
+
+#Decision tree
+
+library(rpart)
+x <- cbind(x_train,y_train)
+# grow tree
+fit <- rpart(y_train ~ ., data = x,method="class")
+summary(fit)
+#Predict Output
+predicted= predict(fit,x_test)
+
+
+#SVM
+
+library(e1071)
+x <- cbind(x_train,y_train)
+# Fitting model
+fit <-svm(y_train ~ ., data = x)
+summary(fit)
+#Predict Output
+predicted= predict(fit,x_test)
+
+# Naive Bayes
+
+library(e1071)
+x <- cbind(x_train,y_train)
+# Fitting model
+fit <-naiveBayes(y_train ~ ., data = x)
+summary(fit)
+#Predict Output
+predicted= predict(fit,x_test)
+
+#kNN (k- Nearest Neighbors)
+
+library(knn)
+x <- cbind(x_train,y_train)
+# Fitting model
+fit <-knn(y_train ~ ., data = x,k=5)
+summary(fit)
+#Predict Output
+predicted= predict(fit,x_test)
+
+#K-Means
+
+library(cluster)
+fit <- kmeans(X, 3) # 5 cluster solution
+
+#Random Forest
+
+library(randomForest)
+x <- cbind(x_train,y_train)
+# Fitting model
+fit <- randomForest(Species ~ ., x,ntree=500)
+summary(fit)
+#Predict Output
+predicted= predict(fit,x_test)
+
+#Dimensionality Reduction Algorithms
+
+library(stats)
+pca <- princomp(train, cor = TRUE)
+train_reduced  <- predict(pca,train)
+test_reduced  <- predict(pca,test)

ml algorithms/DataPreprocessing.py

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+'''
+Created on Apr 25, 2016
+test code
+@author: Wenqiang Feng
+'''
+import pandas as pd
+#import numpy as np
+import matplotlib.pyplot as plt
+from pandas.tools.plotting import scatter_matrix
+from docutils.parsers.rst.directives import path
+
+if __name__ == '__main__':
+    path ='~/Dropbox/MachineLearningAlgorithms/python_code/data/Heart.csv'
+    rawdata = pd.read_csv(path)
+
+    print "data summary"
+    print rawdata.describe()
+
+    # summary plot of the data
+    scatter_matrix(rawdata,figsize=[15,15])
+    plt.show()
+
+    # Histogram
+    rawdata.hist()
+    plt.show()
+
+    # boxplot
+    pd.DataFrame.boxplot(rawdata)
+    plt.show()
+
+
+    print "Raw data size"
+    nrow, ncol = rawdata.shape
+    print nrow, ncol
+
+    path = ('/home/feng/Dropbox/MachineLearningAlgorithms/python_code/data/'
+    'energy_efficiency.xlsx')
+    path
+
+    rawdataEnergy= pd.read_excel(path,sheetname=0)
+
+    nrow=rawdata.shape[0] #gives number of row count
+    ncol=rawdata.shape[1] #gives number of col count
+    print nrow, ncol
+    col_names = rawdata.columns.tolist()
+    print "Column names:"
+    print col_names
+    print "Data Format:"
+    print rawdata.dtypes
+
+    print "\nSample data:"
+    print(rawdata.head(6))
+
+
+    print "\n correlation Matrix"
+    print rawdata.corr()
+
+    # cocorrelation Matrix plot
+    pd.DataFrame.corr(rawdata)
+    plt.show()
+
+    print "\n covariance Matrix"
+    print rawdata.cov()
+
+    print rawdata[['Age','Ca']].corr()
+    pd.DataFrame.corr(rawdata)
+    plt.show()
+
+
+
+    # define colors list, to be used to plot survived either red (=0) or green (=1)
+    colors=['red','green']
+
+    # make a scatter plot
+
+#    rawdata.info()
+
+    from scipy import stats
+    import seaborn as sns # just a conventional alias, don't know why
+    sns.corrplot(rawdata) # compute and plot the pair-wise correlations
+    # save to file, remove the big white borders
+    #plt.savefig('attribute_correlations.png', tight_layout=True)
+    plt.show()
+
+
+    attr = rawdata['Age']
+    sns.distplot(attr)
+    plt.show()
+
+    sns.distplot(attr, kde=False, fit=stats.gamma);
+    plt.show()
+
+    # Two subplots, the axes array is 1-d
+    plt.figure(1)
+    plt.title('Histogram of Age')
+    plt.subplot(211) # 21,1 means first one of 2 rows, 1 col
+    sns.distplot(attr)
+
+    plt.subplot(212) #  21,2 means second one of 2 rows, 1 col
+    sns.distplot(attr, kde=False, fit=stats.gamma);
+
+    plt.show()
+
+
+
+
+
+    

ml algorithms/DataPreprocessing.r

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+rm(list = ls())
+# set the enverionment
+path ='~/Dropbox/MachineLearningAlgorithms/python_code/data/Heart.csv'
+rawdata = read.csv(path)
+
+# summary of the data
+summary(rawdata)
+# plot of the summary
+plot(rawdata)
+
+dim(rawdata)
+head(rawdata)
+tail(rawdata)
+
+colnames(rawdata)
+attach(rawdata)
+
+# get numerical data and remove NAN
+numdata=na.omit(rawdata[,c(1:2,4:12)])
+
+cor(numdata)
+cov(numdata)
+
+dev.off()
+# laod cocorrelation Matrix plot lib
+library(corrplot)
+M <- cor(numdata)
+#par(mfrow =c (1,2))
+#corrplot(M, method = "square")
+corrplot.mixed(M)
+
+
+nrow=nrow(rawdata)
+ncol=ncol(rawdata)
+c(nrow, ncol)
+
+
+
+Nvars=ncol(numdata)
+# checking data format
+typeof(rawdata)
+install.packages("mlbench")
+library(mlbench)
+sapply(rawdata, class)
+
+dev.off()
+name=colnames(numdata)
+Nvars=ncol(numdata)
+# boxplot
+par(mfrow =c (4,3))
+for (i in 1:Nvars)
+{
+  #boxplot(numdata[,i]~numdata[,Nvars],data=data,main=name[i])
+  boxplot(numdata[,i],data=numdata,main=name[i])
+}
+
+# Histogram with normal curve plot
+dev.off()
+Nvars=ncol(numdata)
+name=colnames(numdata)
+par(mfrow =c (3,5))
+for (i in 1:Nvars)
+{
+  x<- numdata[,i]
+  h<-hist(x, breaks=10, freq=TRUE, col="blue", xlab=name[i],main=" ",
+            font.lab=1)
+  axis(1, tck=1, col.ticks="light gray")
+  axis(1, tck=-0.015, col.ticks="black")
+  axis(2, tck=1, col.ticks="light gray", lwd.ticks="1")
+  axis(2, tck=-0.015)
+  xfit<-seq(min(x),max(x),length=40)
+  yfit<-dnorm(xfit,mean=mean(x),sd=sd(x))
+  yfit <- yfit*diff(h$mids[1:2])*length(x)
+  lines(xfit, yfit, col="blue", lwd=2)
+}
+
+
+library(reshape2)
+library(ggplot2)
+d <- melt(diamonds[,-c(2:4)])
+ggplot(d,aes(x = value)) +
+  facet_wrap(~variable,scales = "free_x") +
+  geom_histogram()

ml algorithms/LinearRegression.py

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+#LinearRegression]
+
+#Import Library
+#Import other necessary libraries like pandas, numpy...
+from sklearn import linear_model
+#Load Train and Test datasets
+#Identify feature and response variable(s) and values must be numeric and numpy arrays
+x_train=input_variables_values_training_datasets
+y_train=target_variables_values_training_datasets
+x_test=input_variables_values_test_datasets
+# Create linear regression object
+linear = linear_model.LinearRegression()
+# Train the model using the training sets and check score
+linear.fit(x_train, y_train)
+linear.score(x_train, y_train)
+#Equation coefficient and Intercept
+print('Coefficient: \n', linear.coef_)
+print('Intercept: \n', linear.intercept_)
+#Predict Output
+predicted= linear.predict(x_test)

tex/cites.bib

@@ -1,44 +1,42 @@
 @article { robust,
-autor = "Julien Pinquier, Jean-Luc Rouas and Régine André-Obrecht",
 title = "ROBUST SPEECH/MUSIC CLASSIFICATION IN AUDIO DOCUMENTS",
+author = "Julien Pinquier, Jean-Luc Rouas and Régine André-Obrecht",
 journal = "7th International Conference on Spoken Language Processing [ICSLP2002]",
 year = "2002"
 }
 
 @article { mirex,
-autor = "Nikolaos Tsipas, Lazaros Vrysis, Charalampos Dimoulas and George Papanikolaou",
 title = "MIREX 2015: METHODS FOR SPEECH/MUSIC DETECTION AND CLASSIFICATION",
+author = "Nikolaos Tsipas, Lazaros Vrysis, Charalampos Dimoulas and George Papanikolaou",
 journal = "MIREX 2015 Conference",
 year = "2015"
 }
 
 @article { speech,
-autor = "Baniriskhem K. Khonglah and S.R. Mahadeva Prasanna",
 title = "Speech / music classification using speech-specific features",
+author = "Baniriskhem K. Khonglah and S.R. Mahadeva Prasanna",
 journal = "Digital Signal Processing 48",
 year = "2016"
 }
 
 @article{ cuckoo,
+title = "Speech classification based on cuckoo algorithm and support vector machines",
 author = "Wenlei Shi and Xinhai Fan",
-title = "Speech classification based on cuckoo algorithm and support
+journal = "2nd IEEE International Conference on Computational Intelligence and Applications",	  
-                  vector machines",
-journal = "2nd IEEE International Conference on Computational
-                  Intelligence and Applications",	  
 year = "2017"
 }
 
 @article{ radio,
-author = "Stanisław Kacprzak, Błażej Chwiećko and Bartosz Ziółko",
 title = "Speech/music discrimination for analysis of radio stations",
+author = "Stanisław Kacprzak, Błażej Chwiećko and Bartosz Ziółko",
 journal = "2017 International Conference on Systems, Signals and Image Processing (IWSSIP)",
 year = "2017"
 }
 
 <<<<<<< HEAD
 @article { speech,
-author = "Baniriskhem K. Khonglah and S.R. Mahadeva Prasanna",
 title = "Speech / music classification using speech-specific features",
+author = "Baniriskhem K. Khonglah and S.R. Mahadeva Prasanna",
 journal = "Digital Signal Processing 48",
 year = "2016"
 )