diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..2c48e9a
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,22 @@
+## Core latex/pdflatex auxiliary files:
+*.aux
+*.lof
+*.log
+*.lot
+*.fls
+*.out
+*.toc
+*.fmt
+*.fot
+*.cb
+*.cb2
+
+
+## MAC
+.DS_Store
+
+## PyCharm
+*.idea/*.iml
+
+# auxiliary python files
+*.pyc
\ No newline at end of file
diff --git a/README.md b/README.md
index 49f9246..206b184 100644
--- a/README.md
+++ b/README.md
@@ -6,39 +6,39 @@ MachineLearning
 一些常见的机器学习算法的实现代码，本人学习过程中做的总结，资历尚浅，如有错误请不吝指出。
 
 
-##目录介绍
+## 目录介绍
 
 - **DeepLearning Tutorials**
 
-      这个文件夹下包含一些深度学习算法的实现代码，以及具体的应用实例，包含：
+   这个文件夹下包含一些深度学习算法的实现代码，以及具体的应用实例，包含：
 
-	  [dive_into _keras](https://github.com/wepe/MachineLearning/tree/master/DeepLearning%20Tutorials/dive_into_keras) Keras使用进阶。介绍了怎么保存训练好的CNN模型，怎么将CNN用作特征提取，怎么可视化卷积图。[文章链接](http://blog.csdn.net/u012162613/article/details/45581421)， 更多进阶使用方法：[gist](https://gist.github.com/wepe/a05ad572dca002046de443061909ff7a)
+   [dive_into _keras](https://github.com/wepe/MachineLearning/tree/master/DeepLearning%20Tutorials/dive_into_keras) Keras使用进阶。介绍了怎么保存训练好的CNN模型，怎么将CNN用作特征提取，怎么可视化卷积图。[文章链接](http://blog.csdn.net/u012162613/article/details/45581421)， 更多进阶使用方法：[gist](https://gist.github.com/wepe/a05ad572dca002046de443061909ff7a)
       
-      [keras_usage](https://github.com/wepe/MachineLearning/tree/master/DeepLearning%20Tutorials/keras_usage) 介绍了一个简单易用的深度学习框架keras，用经典的Mnist分类问题对该框架的使用进行说明，训练一个CNN，总共不超过30行代码。[文章链接](http://blog.csdn.net/u012162613/article/details/45397033)
+   [keras_usage](https://github.com/wepe/MachineLearning/tree/master/DeepLearning%20Tutorials/keras_usage) 介绍了一个简单易用的深度学习框架keras，用经典的Mnist分类问题对该框架的使用进行说明，训练一个CNN，总共不超过30行代码。[文章链接](http://blog.csdn.net/u012162613/article/details/45397033)
 
-      [FaceRecognition_CNN(olivettifaces)](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/FaceRecognition_CNN(olivettifaces))
+   [FaceRecognition_CNN(olivettifaces)](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/FaceRecognition_CNN(olivettifaces))
       将卷积神经网络CNN应用于人脸识别的一个demo，人脸数据库采用olivettifaces，CNN模型参考LeNet5，基于python+theano+numpy+PIL实现。详细介绍这个demo的文章：[文章链接](http://blog.csdn.net/u012162613/article/details/43277187)
 
 
-      [cnn_LeNet](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/cnn_LeNet)  CNN卷积神经网络算法的实现，模型为简化版的LeNet，应用于MNIST数据集（手写数字），来自于DeepLearning.net上的一个教程，基于python+theano，我用了中文将原始的代码进行详细的解读，并简单总结了CNN算法，相应的文章发在：[文章链接](http://blog.csdn.net/u012162613/article/details/43225445)
+   [cnn_LeNet](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/cnn_LeNet)  CNN卷积神经网络算法的实现，模型为简化版的LeNet，应用于MNIST数据集（手写数字），来自于DeepLearning.net上的一个教程，基于python+theano，我用了中文将原始的代码进行详细的解读，并简单总结了CNN算法，相应的文章发在：[文章链接](http://blog.csdn.net/u012162613/article/details/43225445)
 
-      [mlp](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/mlp)  多层感知机算法的实现，代码实现了最简单的三层感知机，并应用于MNIST数据集，来自DeepLearning.net上的一个教程，基于python+theano，我写了一篇文章总结介绍了MLP算法，同时用中文详细解读了原始的代码：[文章链接](http://blog.csdn.net/u012162613/article/details/43221829)
+   [mlp](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/mlp)  多层感知机算法的实现，代码实现了最简单的三层感知机，并应用于MNIST数据集，来自DeepLearning.net上的一个教程，基于python+theano，我写了一篇文章总结介绍了MLP算法，同时用中文详细解读了原始的代码：[文章链接](http://blog.csdn.net/u012162613/article/details/43221829)
 
-      [Softmax_sgd(or logistic_sgd)](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/Softmax_sgd(or%20logistic_sgd)) Softmax回归算法的实现，应用于MNIST数据集，基于Python+theano，来自DeepLearning.net上的一个教程，基于python+theano，我写了一篇文章介绍了Softmax回归算法，同时用中文详细解读了原始的代码：[文章链接](http://blog.csdn.net/u012162613/article/details/43157801)
+   [Softmax_sgd(or logistic_sgd)](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/Softmax_sgd(or%20logistic_sgd)) Softmax回归算法的实现，应用于MNIST数据集，基于Python+theano，来自DeepLearning.net上的一个教程，基于python+theano，我写了一篇文章介绍了Softmax回归算法，同时用中文详细解读了原始的代码：[文章链接](http://blog.csdn.net/u012162613/article/details/43157801)
 
 - **PCA**
 
-      基于python+numpy实现了主成份分析PCA算法，这里详细地介绍了PCA算法，以及代码开发流程：[文章链接](http://blog.csdn.net/u012162613/article/details/42177327)
+   基于python+numpy实现了主成份分析PCA算法，这里详细地介绍了PCA算法，以及代码开发流程：[文章链接](http://blog.csdn.net/u012162613/article/details/42177327)
 
 - **kNN**
       
-      基于python+numpy实现了K近邻算法，并将其应用在MNIST数据集上，详细的介绍：[文章链接](http://blog.csdn.net/u012162613/article/details/41768407)
+   基于python+numpy实现了K近邻算法，并将其应用在MNIST数据集上，详细的介绍：[文章链接](http://blog.csdn.net/u012162613/article/details/41768407)
 
 - **logistic regression**
 
-	  - 基于C++以及线性代数库Eigen实现的logistic回归，[代码](https://github.com/wepe/MachineLearning/tree/master/logistic%20regression/use_cpp_and_eigen)
+   - 基于C++以及线性代数库Eigen实现的logistic回归，[代码](https://github.com/wepe/MachineLearning/tree/master/logistic%20regression/use_cpp_and_eigen)
 
-      - 基于python+numpy实现了logistic回归（二类别），详细的介绍：[文章链接](http://blog.csdn.net/u012162613/article/details/41844495)
+   - 基于python+numpy实现了logistic回归（二类别），详细的介绍：[文章链接](http://blog.csdn.net/u012162613/article/details/41844495)
 
 - **ManifoldLearning**
 
@@ -48,6 +48,11 @@ MachineLearning
 
 	[libsvm liblinear-usage](https://github.com/wepe/MachineLearning/tree/master/SVM/libsvm%20liblinear-usage) 对使用广泛的libsvm、liblinear的使用方法进行了总结，详细介绍：[文章链接](http://blog.csdn.net/u012162613/article/details/45206813)
 
+    [SVM by SMO](./SVM/SVM_by_SMO) - 用SMO实现了SVM
+
+    [SVM by QP](./SVM/SVM_by_QP) - 用二次编程（QP）实现了SVM
+
+
 - **GMM**
 
 	GMM和k-means作为EM算法的应用，在某种程度有些相似之处，不过GMM明显学习出一些概率密度函数来，结合相关理解写成python版本，详细介绍：[文章链接](http://blog.csdn.net/gugugujiawei/article/details/45583051)
@@ -64,8 +69,12 @@ MachineLearning
 
 	朴素贝叶斯算法的理论推导，以及三种常见模型（多项式模型，高斯模型，伯努利模型）的介绍与编程实现（基于Python，Numpy）。[文章链接](http://blog.csdn.net/u012162613/article/details/48323777)
 
-##Contributor
+- **Ridge and Kernel Ridge**
+
+    介绍了Ridge回归和它的Kernel版本。[代码](./Ridge/kernel_ridge/kernel_ridge.py)
+
+## Contributor
 
 - [wepon](https://github.com/wepe)
 - [Gogary](https://github.com/enjoyhot)
- 
+- [Locky](https://github.com/junlulocky)
diff --git a/Ridge/README.md b/Ridge/README.md
new file mode 100644
index 0000000..6e786a3
--- /dev/null
+++ b/Ridge/README.md
@@ -0,0 +1,18 @@
+Ridge and Kernel Ridge
+======================
+
+## Ridge
+
+TODO
+
+## Kernel Ridge
+
+See [run_kernel_ridge.py](./run_kernel_ridge.py) for an example for Kernel Ridge
+
+## Contributor
+
+- [junlulocky](https://github.com/junlulocky)
+ 
+## Reference
+
+- Welling, Max. "Kernel ridge Regression." Max Welling's Classnotes in Machine Learning (2013): 1-3.
\ No newline at end of file
diff --git a/Ridge/kernel_ridge/kernel_ridge.py b/Ridge/kernel_ridge/kernel_ridge.py
new file mode 100644
index 0000000..6abec91
--- /dev/null
+++ b/Ridge/kernel_ridge/kernel_ridge.py
@@ -0,0 +1,90 @@
+import scipy as sp
+from numpy.linalg import inv
+import numpy as np
+from scipy import linalg
+
+
+class KernelRidge():
+    """
+        Simple implementation of a Kernel Ridge Regression using the
+        closed form for training.
+        Doc: https://www.ics.uci.edu/~welling/classnotes/papers_class/Kernel-Ridge.pdf
+    """
+
+    def __init__(self, kernel_type='linear', C=1.0, gamma=5.0):
+        """
+        :param kernel_type: Kernel type to use in training.
+                        'linear' use linear kernel function.
+                        'quadratic' use quadratic kernel function.
+                        'gaussian' use gaussian kernel function
+        :param C: Value of regularization parameter C
+        :param gamma: parameter for gaussian kernel or Polynomial kernel
+        """
+        self.kernels = {
+            'linear': self.kernel_linear,
+            'quadratic': self.kernel_quadratic,
+            'gaussian': self.kernel_gaussian
+        }
+        self.kernel_type = kernel_type
+        self.kernel = self.kernels[self.kernel_type]
+        self.C = C
+        self.gamma = gamma
+
+    # Define kernels
+    def kernel_linear(self, x1, x2):
+        return np.dot(x1, x2.T)
+
+    def kernel_quadratic(self, x1, x2):
+        return (np.dot(x1, x2.T) ** 2)
+
+    def kernel_gaussian(self, x1, x2, gamma=5.0):
+        gamma = self.gamma
+        return np.exp(-linalg.norm(x1 - x2) ** 2 / (2 * (gamma ** 2)))
+
+    def compute_kernel_matrix(self, X1, X2):
+        """
+        compute kernel matrix (gram matrix) give two input matrix
+        """
+
+        # sample size
+        n1 = X1.shape[0]
+        n2 = X2.shape[0]
+
+        # Gram matrix
+        K = np.zeros((n1, n2))
+        for i in range(n1):
+            for j in range(n2):
+                K[i, j] = self.kernel(X1[i], X2[j])
+
+        return K
+
+
+    def fit(self, X, y):
+        """
+        training KRR
+        :param X: training X
+        :param y: training y
+        :return: alpha vector, see document TODO
+        """
+        K = self.compute_kernel_matrix(X, X)
+
+        self.alphas = sp.dot(inv(K + self.C * np.eye(np.shape(K)[0])),
+                        y.transpose())
+
+        return self.alphas
+
+    def predict(self, x_train, x_test):
+        """
+
+        :param x_train: DxNtr array of Ntr train data points
+                        with D features
+        :param x_test:  DxNte array of Nte test data points
+                        with D features
+        :return: y_test, D2xNte array
+        """
+
+        k = self.compute_kernel_matrix(x_test, x_train)
+
+        y_test = sp.dot(k, self.alphas)
+        return y_test.transpose()
+
diff --git a/Ridge/kernel_ridge/kernel_ridge.pyc b/Ridge/kernel_ridge/kernel_ridge.pyc
new file mode 100644
index 0000000..6116f24
Binary files /dev/null and b/Ridge/kernel_ridge/kernel_ridge.pyc differ
diff --git a/Ridge/kernel_ridge/run_kernel_ridge.py b/Ridge/kernel_ridge/run_kernel_ridge.py
new file mode 100644
index 0000000..4d614d3
--- /dev/null
+++ b/Ridge/kernel_ridge/run_kernel_ridge.py
@@ -0,0 +1,61 @@
+import csv, os, sys
+import numpy as np
+from kernel_ridge import KernelRidge
+filepath = os.path.dirname(os.path.abspath(__file__))
+
+def readData(filename, header=True):
+    data, header = [], None
+    with open(filename, 'rb') as csvfile:
+        spamreader = csv.reader(csvfile, delimiter=',')
+        if header:
+            header = spamreader.next()
+        for row in spamreader:
+            data.append(row)
+    return (np.array(data), np.array(header))
+
+def calc_mse(y, y_hat):
+    return np.nanmean(((y - y_hat) ** 2))
+
+def test_main(filename='small_data/iris-virginica.txt', C=1.0, kernel_type='linear'):
+    # Load data
+    (data, _) = readData('%s/%s' % (filepath, filename), header=False)
+    data = data.astype(float)
+
+    # Split data
+    X, y = data[:,0:-1], data[:,-1].astype(int)
+    y = y[np.newaxis,:]
+    print X.shape
+    print y.shape
+
+
+
+    # fit our model
+    model = KernelRidge(kernel_type='gaussian', C=0.1, gamma=5.0)
+    model.fit(X, y)
+    y_hat = model.predict(x_train=X, x_test=X)
+    mse = calc_mse(y, y_hat) # Calculate accuracy
+    print("mse of KRR:\t%.3f" % (mse))
+
+    # fit linear model for test
+    from sklearn import linear_model
+    ls = linear_model.LinearRegression()
+    ls.fit(X, y[0,:])
+    y_ls = ls.predict(X)
+    mse = calc_mse(y, y_ls)
+    print("mse of LS (from sklearn):\t%.3f" % (mse))
+
+    # fit KRR from sklearn for test
+    from sklearn.kernel_ridge import KernelRidge as KR2
+    kr2 = KR2(kernel='rbf', gamma=5, alpha=10)
+    kr2.fit(X, y[0, :])
+    y_krr = kr2.predict(X)
+    mse = calc_mse(y, y_krr)
+    print("mse of KRR (from sklearn):\t%.3f" % (mse))
+
+
+
+
+
+if __name__ == '__main__':
+
+    test_main(filename='./small_data/iris-slwc.txt')
diff --git a/Ridge/kernel_ridge/small_data/iris-slwc.txt b/Ridge/kernel_ridge/small_data/iris-slwc.txt
new file mode 100755
index 0000000..685db1a
--- /dev/null
+++ b/Ridge/kernel_ridge/small_data/iris-slwc.txt
@@ -0,0 +1,150 @@
+5.9,3,1
+6.9,3.1,1
+6.6,2.9,1
+4.6,3.2,-1
+6,2.2,1
+4.7,3.2,-1
+6.5,3,1
+5.8,2.7,1
+6.7,3.1,1
+6.7,2.5,1
+5.1,3.7,-1
+5.1,3.8,-1
+5.7,3,1
+6.1,3,1
+4.9,3.1,-1
+5,3.4,-1
+5,3.4,-1
+5.7,2.8,1
+5,3.3,-1
+7.2,3.2,1
+5.9,3,1
+6.5,3,1
+5.7,4.4,-1
+5.5,2.5,1
+4.9,2.5,1
+5,3.5,-1
+5.5,2.3,1
+4.6,3.1,-1
+7.2,3,1
+6.8,3.2,1
+5.4,3.9,-1
+5,3.2,-1
+5.7,2.5,1
+5.8,2.6,1
+5.1,2.5,1
+5.6,2.5,1
+5.8,2.7,1
+5.1,3.8,-1
+6.3,2.3,1
+6.3,2.5,1
+5.6,3,1
+6.1,3,1
+6.8,3,1
+7.3,2.9,1
+5.6,2.7,1
+4.8,3,-1
+7.1,3,1
+5.7,2.6,1
+5.3,3.7,-1
+5.7,3.8,-1
+5.7,2.9,1
+5.6,2.8,1
+4.4,3,-1
+6.3,3.3,1
+5.4,3.4,-1
+6.3,3.4,1
+6.9,3.1,1
+7.7,3,1
+6.1,2.8,1
+5.6,2.9,1
+6.1,2.6,1
+6.4,2.7,1
+5,3.5,-1
+5.1,3.3,-1
+5.6,3,1
+5.4,3,1
+5.8,2.8,1
+4.9,3.1,-1
+4.6,3.6,-1
+5.2,3.4,-1
+7.9,3.8,1
+7.7,2.6,1
+6.1,2.8,1
+5.5,3.5,-1
+4.6,3.4,-1
+4.7,3.2,-1
+4.4,2.9,-1
+6.2,2.8,1
+4.8,3,-1
+6,2.9,1
+6.2,3.4,1
+5,2.3,1
+6.4,3.2,1
+6.3,2.9,1
+6.7,3,1
+5,2,1
+5.9,3.2,1
+6.7,3.3,1
+5.4,3.9,-1
+6.3,2.7,1
+4.8,3.4,-1
+4.4,3.2,-1
+6.4,3.2,1
+6.2,2.2,1
+6,2.2,1
+7.4,2.8,1
+4.9,2.4,1
+7,3.2,1
+5.5,2.4,1
+6.3,3.3,1
+6.8,2.8,1
+6.1,2.9,1
+6.5,3.2,1
+6.7,3.3,1
+6.7,3.1,1
+4.8,3.4,-1
+4.9,3,-1
+6.9,3.2,1
+4.5,2.3,-1
+4.3,3,-1
+5.2,2.7,1
+5,3.6,-1
+6.4,2.9,1
+5.2,3.5,-1
+5.8,2.7,1
+5.5,4.2,-1
+7.6,3,1
+6.3,2.8,1
+6.4,3.1,1
+6.3,2.5,1
+5.8,2.7,1
+5,3,-1
+6.7,3.1,1
+6,2.7,1
+5.1,3.5,-1
+4.8,3.1,-1
+5.7,2.8,1
+5.1,3.8,-1
+6.6,3,1
+6.4,2.8,1
+5.2,4.1,-1
+6.4,2.8,1
+7.7,2.8,1
+5.8,4,-1
+4.9,3.1,-1
+5.4,3.7,-1
+5.1,3.5,-1
+6,3.4,1
+6.5,3,1
+5.5,2.4,1
+7.2,3.6,1
+6.9,3.1,1
+6.2,2.9,1
+6.5,2.8,1
+6,3,1
+5.4,3.4,-1
+5.5,2.6,1
+6.7,3,1
+7.7,3.8,1
+5.1,3.4,-1
diff --git a/Ridge/kernel_ridge/small_data/iris-versicolor.txt b/Ridge/kernel_ridge/small_data/iris-versicolor.txt
new file mode 100755
index 0000000..b09508e
--- /dev/null
+++ b/Ridge/kernel_ridge/small_data/iris-versicolor.txt
@@ -0,0 +1,150 @@
+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
+4.9,3.1,1.5,0.1,-1
+5.4,3.7,1.5,0.2,-1
+4.8,3.4,1.6,0.2,-1
+4.8,3,1.4,0.1,-1
+4.3,3,1.1,0.1,-1
+5.8,4,1.2,0.2,-1
+5.7,4.4,1.5,0.4,-1
+5.4,3.9,1.3,0.4,-1
+5.1,3.5,1.4,0.3,-1
+5.7,3.8,1.7,0.3,-1
+5.1,3.8,1.5,0.3,-1
+5.4,3.4,1.7,0.2,-1
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diff --git a/Ridge/kernel_ridge/small_data/iris-virginica.txt b/Ridge/kernel_ridge/small_data/iris-virginica.txt
new file mode 100755
index 0000000..b2a701b
--- /dev/null
+++ b/Ridge/kernel_ridge/small_data/iris-virginica.txt
@@ -0,0 +1,150 @@
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diff --git a/SVM/SVM_by_QP/README.md b/SVM/SVM_by_QP/README.md
new file mode 100644
index 0000000..c385ed9
--- /dev/null
+++ b/SVM/SVM_by_QP/README.md
@@ -0,0 +1,7 @@
+# SVM Python
+
+This is an example of SVM implementation by Python based on cvxopt. 
+
+The quadratic programming function, please refer to http://cvxopt.org/userguide/coneprog.html?highlight=qp#cvxopt.solvers.qp
+
+The theory of SVM, please refer to the four SVM slides included in this project.
diff --git a/SVM/SVM_by_QP/SVCQP.py b/SVM/SVM_by_QP/SVCQP.py
new file mode 100644
index 0000000..e4eb8fb
--- /dev/null
+++ b/SVM/SVM_by_QP/SVCQP.py
@@ -0,0 +1,100 @@
+import numpy as np
+from numpy import linalg
+import cvxopt
+import cvxopt.solvers
+
+## define kenrel functions
+def linear_kernel(x1, x2):
+    return np.dot(x1, x2)
+
+def polynomial_kernel(x, y, p=3):
+    return (1 + np.dot(x, y)) ** p
+
+def gaussian_kernel(x, y, sigma=5.0):
+    return np.exp(-linalg.norm(x-y)**2 / (2 * (sigma ** 2)))
+## end define kernel functions
+
+class SVM(object):
+    """
+    Suppoet vector classification by quadratic programming
+    """
+
+    def __init__(self, kernel=linear_kernel, C=None):
+        """
+
+        :param kernel: kernel types, should be in the kernel function list above
+        :param C:
+        """
+        self.kernel = kernel
+        self.C = C
+        if self.C is not None: self.C = float(self.C)
+
+    def fit(self, X, y):
+        n_samples, n_features = X.shape
+
+        # Gram matrix
+        K = np.zeros((n_samples, n_samples))
+        for i in range(n_samples):
+            for j in range(n_samples):
+                K[i,j] = self.kernel(X[i], X[j])
+
+        P = cvxopt.matrix(np.outer(y,y) * K)
+        q = cvxopt.matrix(np.ones(n_samples) * -1)
+        A = cvxopt.matrix(y, (1,n_samples))
+        b = cvxopt.matrix(0.0)
+
+        if self.C is None:
+            G = cvxopt.matrix(np.diag(np.ones(n_samples) * -1))
+            h = cvxopt.matrix(np.zeros(n_samples))
+        else:
+            tmp1 = np.diag(np.ones(n_samples) * -1)
+            tmp2 = np.identity(n_samples)
+            G = cvxopt.matrix(np.vstack((tmp1, tmp2)))
+            tmp1 = np.zeros(n_samples)
+            tmp2 = np.ones(n_samples) * self.C
+            h = cvxopt.matrix(np.hstack((tmp1, tmp2)))
+
+        # solve QP problem, DOC: http://cvxopt.org/userguide/coneprog.html?highlight=qp#cvxopt.solvers.qp
+        solution = cvxopt.solvers.qp(P, q, G, h, A, b)
+
+        # Lagrange multipliers
+        a = np.ravel(solution['x'])
+
+        # Support vectors have non zero lagrange multipliers
+        sv = a > 1e-5
+        ind = np.arange(len(a))[sv]
+        self.a = a[sv]
+        self.sv = X[sv]
+        self.sv_y = y[sv]
+        print "%d support vectors out of %d points" % (len(self.a), n_samples)
+
+        # Intercept
+        self.b = 0
+        for n in range(len(self.a)):
+            self.b += self.sv_y[n]
+            self.b -= np.sum(self.a * self.sv_y * K[ind[n],sv])
+        self.b /= len(self.a)
+
+        # Weight vector
+        if self.kernel == linear_kernel:
+            self.w = np.zeros(n_features)
+            for n in range(len(self.a)):
+                self.w += self.a[n] * self.sv_y[n] * self.sv[n]
+        else:
+            self.w = None
+
+    def project(self, X):
+        if self.w is not None:
+            return np.dot(X, self.w) + self.b
+        else:
+            y_predict = np.zeros(len(X))
+            for i in range(len(X)):
+                s = 0
+                for a, sv_y, sv in zip(self.a, self.sv_y, self.sv):
+                    s += a * sv_y * self.kernel(X[i], sv)
+                y_predict[i] = s
+            return y_predict + self.b
+
+    def predict(self, X):
+        return np.sign(self.project(X))
+
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diff --git a/SVM/SVM_by_QP/doc/SVM_quick_doc.pdf b/SVM/SVM_by_QP/doc/SVM_quick_doc.pdf
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diff --git a/SVM/SVM_by_QP/testSVM.py b/SVM/SVM_by_QP/testSVM.py
new file mode 100644
index 0000000..fc3ec3a
--- /dev/null
+++ b/SVM/SVM_by_QP/testSVM.py
@@ -0,0 +1,153 @@
+from SVCQP import *
+import pylab as pl
+
+def gen_lin_separable_data():
+    # generate training data in the 2-d case
+    mean1 = np.array([0, 2])
+    mean2 = np.array([2, 0])
+    cov = np.array([[0.8, 0.6], [0.6, 0.8]])
+    X1 = np.random.multivariate_normal(mean1, cov, 100)
+    y1 = np.ones(len(X1))
+    X2 = np.random.multivariate_normal(mean2, cov, 100)
+    y2 = np.ones(len(X2)) * -1
+    return X1, y1, X2, y2
+
+def gen_non_lin_separable_data():
+    mean1 = [-1, 2]
+    mean2 = [1, -1]
+    mean3 = [4, -4]
+    mean4 = [-4, 4]
+    cov = [[1.0,0.8], [0.8, 1.0]]
+    X1 = np.random.multivariate_normal(mean1, cov, 50)
+    X1 = np.vstack((X1, np.random.multivariate_normal(mean3, cov, 50)))
+    y1 = np.ones(len(X1))
+    X2 = np.random.multivariate_normal(mean2, cov, 50)
+    X2 = np.vstack((X2, np.random.multivariate_normal(mean4, cov, 50)))
+    y2 = np.ones(len(X2)) * -1
+    return X1, y1, X2, y2
+
+def gen_lin_separable_overlap_data():
+    # generate training data in the 2-d case
+    mean1 = np.array([0, 2])
+    mean2 = np.array([2, 0])
+    cov = np.array([[1.5, 1.0], [1.0, 1.5]])
+    X1 = np.random.multivariate_normal(mean1, cov, 100)
+    y1 = np.ones(len(X1))
+    X2 = np.random.multivariate_normal(mean2, cov, 100)
+    y2 = np.ones(len(X2)) * -1
+    return X1, y1, X2, y2
+
+def split_train(X1, y1, X2, y2):
+    X1_train = X1[:90]
+    y1_train = y1[:90]
+    X2_train = X2[:90]
+    y2_train = y2[:90]
+    X_train = np.vstack((X1_train, X2_train))
+    y_train = np.hstack((y1_train, y2_train))
+    return X_train, y_train
+
+def split_test(X1, y1, X2, y2):
+    X1_test = X1[90:]
+    y1_test = y1[90:]
+    X2_test = X2[90:]
+    y2_test = y2[90:]
+    X_test = np.vstack((X1_test, X2_test))
+    y_test = np.hstack((y1_test, y2_test))
+    return X_test, y_test
+
+def plot_margin(X1_train, X2_train, clf):
+    def f(x, w, b, c=0):
+        # given x, return y such that [x,y] in on the line
+        # w.x + b = c
+        return (-w[0] * x - b + c) / w[1]
+
+    pl.plot(X1_train[:,0], X1_train[:,1], "ro")
+    pl.plot(X2_train[:,0], X2_train[:,1], "bo")
+    pl.scatter(clf.sv[:,0], clf.sv[:,1], s=100, c="g")
+
+    # w.x + b = 0
+    a0 = -4; a1 = f(a0, clf.w, clf.b)
+    b0 = 4; b1 = f(b0, clf.w, clf.b)
+    pl.plot([a0,b0], [a1,b1], "k")
+
+    # w.x + b = 1
+    a0 = -4; a1 = f(a0, clf.w, clf.b, 1)
+    b0 = 4; b1 = f(b0, clf.w, clf.b, 1)
+    pl.plot([a0,b0], [a1,b1], "k--")
+
+    # w.x + b = -1
+    a0 = -4; a1 = f(a0, clf.w, clf.b, -1)
+    b0 = 4; b1 = f(b0, clf.w, clf.b, -1)
+    pl.plot([a0,b0], [a1,b1], "k--")
+
+    pl.axis("tight")
+    pl.show()
+
+def plot_contour(X1_train, X2_train, clf):
+    pl.plot(X1_train[:,0], X1_train[:,1], "ro")
+    pl.plot(X2_train[:,0], X2_train[:,1], "bo")
+    pl.scatter(clf.sv[:,0], clf.sv[:,1], s=100, c="g")
+
+    X1, X2 = np.meshgrid(np.linspace(-6,6,50), np.linspace(-6,6,50))
+    X = np.array([[x1, x2] for x1, x2 in zip(np.ravel(X1), np.ravel(X2))])
+    Z = clf.project(X).reshape(X1.shape)
+    pl.contour(X1, X2, Z, [0.0], colors='k', linewidths=1, origin='lower')
+    pl.contour(X1, X2, Z + 1, [0.0], colors='grey', linewidths=1, origin='lower')
+    pl.contour(X1, X2, Z - 1, [0.0], colors='grey', linewidths=1, origin='lower')
+
+    pl.axis("tight")
+    pl.show()
+
+def test_linear():
+    X1, y1, X2, y2 = gen_lin_separable_data()
+    X_train, y_train = split_train(X1, y1, X2, y2)
+    X_test, y_test = split_test(X1, y1, X2, y2)
+
+    clf = SVM()
+    clf.fit(X_train, y_train)
+
+    y_predict = clf.predict(X_test)
+    correct = np.sum(y_predict == y_test)
+    print "%d out of %d predictions correct" % (correct, len(y_predict))
+
+    plot_margin(X_train[y_train==1], X_train[y_train==-1], clf)
+
+def test_non_linear():
+    X1, y1, X2, y2 = gen_non_lin_separable_data()
+    X_train, y_train = split_train(X1, y1, X2, y2)
+    X_test, y_test = split_test(X1, y1, X2, y2)
+
+    # X_train = np.load('inputClf/X_train.npy')
+    # y_train = np.load('inputClf/y_train.npy')
+    # X_test = np.load('inputClf/X_test.npy')
+    # y_test = np.load('inputClf/y_test.npy')
+    clf = SVM(gaussian_kernel, C=1)
+    clf.fit(X_train, y_train)
+
+    y_predict = clf.predict(X_test)
+    correct = np.sum(y_predict == y_test)
+    print "%d out of %d predictions correct" % (correct, len(y_predict))
+
+    plot_contour(X_train[y_train==1], X_train[y_train==-1], clf)
+
+def test_soft():
+    X1, y1, X2, y2 = gen_lin_separable_overlap_data()
+    X_train, y_train = split_train(X1, y1, X2, y2)
+    X_test, y_test = split_test(X1, y1, X2, y2)
+
+
+
+    clf = SVM(C=0.1)
+    clf.fit(X_train, y_train)
+
+    y_predict = clf.predict(X_test)
+    correct = np.sum(y_predict == y_test)
+    print "%d out of %d predictions correct" % (correct, len(y_predict))
+
+    plot_contour(X_train[y_train==1], X_train[y_train==-1], clf)
+
+
+if __name__ == "__main__":
+
+    test_non_linear()
+    #test_soft()
\ No newline at end of file
diff --git a/SVM/SVM_by_SMO/README.md b/SVM/SVM_by_SMO/README.md
new file mode 100755
index 0000000..c624620
--- /dev/null
+++ b/SVM/SVM_by_SMO/README.md
@@ -0,0 +1,33 @@
+# SVM
+
+Simple implementation of a Support Vector Classification using the Sequential Minimal Optimization (SMO) algorithm for 
+training.
+
+## Supported python versions:
+* Python 2.7
+* Python 3.4
+
+## Python package dependencies
+* Numpy
+
+# Documentation
+
+Setup model (following parameters are default)
+
+```python
+
+from SVCSMO import SVCSMO
+model = SVCSMO(max_iter=10000, kernel_type='linear', C=1.0, epsilon=0.001)
+```
+
+Train model
+
+```python
+model.fit(X, y)
+```
+
+Predict new observations
+
+```python
+y_hat = model.predict(X_test)
+```
diff --git a/SVM/SVM_by_SMO/SVCSMO.py b/SVM/SVM_by_SMO/SVCSMO.py
new file mode 100755
index 0000000..108cf0c
--- /dev/null
+++ b/SVM/SVM_by_SMO/SVCSMO.py
@@ -0,0 +1,114 @@
+from __future__ import division, print_function
+from numpy import linalg
+import os
+import numpy as np
+import random as rnd
+filepath = os.path.dirname(os.path.abspath(__file__))
+
+class SVCSMO():
+    """
+        Simple implementation of a Support Vector Classification using the
+        Sequential Minimal Optimization (SMO) algorithm for training.
+    """
+    def __init__(self, max_iter=10000, kernel_type='linear', C=1.0, epsilon=0.001, sigma=5.0):
+        """
+        :param max_iter: maximum iteration
+        :param kernel_type: Kernel type to use in training.
+                        'linear' use linear kernel function.
+                        'quadratic' use quadratic kernel function.
+                        'gaussian' use gaussian kernel function
+        :param C: Value of regularization parameter C
+        :param epsilon: Convergence value.
+        :param sigma: parameter for gaussian kernel
+        """
+        self.kernels = {
+            'linear' : self.kernel_linear,
+            'quadratic' : self.kernel_quadratic,
+            'gaussian' : self.kernel_gaussian
+        }
+        self.max_iter = max_iter
+        self.kernel_type = kernel_type
+        self.C = C
+        self.epsilon = epsilon
+        self.sigma = sigma
+    def fit(self, X, y):
+        # Initialization
+        n, d = X.shape[0], X.shape[1]
+        alpha = np.zeros((n))
+        kernel = self.kernels[self.kernel_type]
+        count = 0
+        while True:
+            count += 1
+            alpha_prev = np.copy(alpha)
+            for j in range(0, n):
+                i = self.get_rnd_int(0, n-1, j) # Get random int i~=j
+                x_i, x_j, y_i, y_j = X[i,:], X[j,:], y[i], y[j]
+                k_ij = kernel(x_i, x_i) + kernel(x_j, x_j) - 2 * kernel(x_i, x_j)
+                if k_ij == 0:
+                    continue
+                alpha_prime_j, alpha_prime_i = alpha[j], alpha[i]
+                (L, H) = self.compute_L_H(self.C, alpha_prime_j, alpha_prime_i, y_j, y_i)
+
+                # Compute model parameters
+                self.w = self.calc_w(alpha, y, X)
+                self.b = self.calc_b(X, y, self.w)
+
+                # Compute E_i, E_j
+                E_i = self.E(x_i, y_i, self.w, self.b)
+                E_j = self.E(x_j, y_j, self.w, self.b)
+
+                # Set new alpha values
+                alpha[j] = alpha_prime_j + float(y_j * (E_i - E_j))/k_ij
+                alpha[j] = max(alpha[j], L)
+                alpha[j] = min(alpha[j], H)
+
+                alpha[i] = alpha_prime_i + y_i*y_j * (alpha_prime_j - alpha[j])
+
+            # Check convergence
+            diff = np.linalg.norm(alpha - alpha_prev)
+            if diff < self.epsilon:
+                break
+
+            if count >= self.max_iter:
+                print("Iteration number exceeded the max of %d iterations" % (self.max_iter))
+                return
+        # Compute final model parameters
+        self.b = self.calc_b(X, y, self.w)
+        if self.kernel_type == 'linear':
+            self.w = self.calc_w(alpha, y, X)
+        # Get support vectors
+        alpha_idx = np.where(alpha > 0)[0]
+        support_vectors = X[alpha_idx, :]
+        return support_vectors, count
+    def predict(self, X):
+        return self.h(X, self.w, self.b)
+    def calc_b(self, X, y, w):
+        b_tmp = y - np.dot(w.T, X.T)
+        return np.mean(b_tmp)
+    def calc_w(self, alpha, y, X):
+        return np.dot(alpha * y, X)
+    # Prediction
+    def h(self, X, w, b):
+        return np.sign(np.dot(w.T, X.T) + b).astype(int)
+    # Prediction error
+    def E(self, x_k, y_k, w, b):
+        return self.h(x_k, w, b) - y_k
+    def compute_L_H(self, C, alpha_prime_j, alpha_prime_i, y_j, y_i):
+        if(y_i != y_j):
+            return (max(0, alpha_prime_j - alpha_prime_i), min(C, C - alpha_prime_i + alpha_prime_j))
+        else:
+            return (max(0, alpha_prime_i + alpha_prime_j - C), min(C, alpha_prime_i + alpha_prime_j))
+    def get_rnd_int(self, a,b,z):
+        i = z
+        while i == z:
+            i = rnd.randint(a,b)
+        return i
+    # Define kernels
+    def kernel_linear(self, x1, x2):
+        return np.dot(x1, x2.T)
+    def kernel_quadratic(self, x1, x2):
+        return (np.dot(x1, x2.T) ** 2)
+    def kernel_gaussian(self, x1, x2, sigma=5.0):
+        if self.sigma:
+            sigma = self.sigma
+        return np.exp(-linalg.norm(x1-x2)**2 / (2 * (sigma ** 2)))
diff --git a/SVM/SVM_by_SMO/SVCSMO.pyc b/SVM/SVM_by_SMO/SVCSMO.pyc
new file mode 100644
index 0000000..80afb58
Binary files /dev/null and b/SVM/SVM_by_SMO/SVCSMO.pyc differ
diff --git a/SVM/SVM_by_SMO/small_data/iris-slwc.txt b/SVM/SVM_by_SMO/small_data/iris-slwc.txt
new file mode 100755
index 0000000..685db1a
--- /dev/null
+++ b/SVM/SVM_by_SMO/small_data/iris-slwc.txt
@@ -0,0 +1,150 @@
+5.9,3,1
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diff --git a/SVM/SVM_by_SMO/small_data/iris-versicolor.txt b/SVM/SVM_by_SMO/small_data/iris-versicolor.txt
new file mode 100755
index 0000000..b09508e
--- /dev/null
+++ b/SVM/SVM_by_SMO/small_data/iris-versicolor.txt
@@ -0,0 +1,150 @@
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diff --git a/SVM/SVM_by_SMO/small_data/iris-virginica.txt b/SVM/SVM_by_SMO/small_data/iris-virginica.txt
new file mode 100755
index 0000000..b2a701b
--- /dev/null
+++ b/SVM/SVM_by_SMO/small_data/iris-virginica.txt
@@ -0,0 +1,150 @@
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diff --git a/SVM/SVM_by_SMO/testSVM-SMO.py b/SVM/SVM_by_SMO/testSVM-SMO.py
new file mode 100755
index 0000000..2fe1929
--- /dev/null
+++ b/SVM/SVM_by_SMO/testSVM-SMO.py
@@ -0,0 +1,67 @@
+from __future__ import division, print_function
+import csv, os, sys
+import numpy as np
+from SVCSMO import SVCSMO
+filepath = os.path.dirname(os.path.abspath(__file__))
+
+def readData(filename, header=True):
+    data, header = [], None
+    with open(filename, 'rb') as csvfile:
+        spamreader = csv.reader(csvfile, delimiter=',')
+        if header:
+            header = spamreader.next()
+        for row in spamreader:
+            data.append(row)
+    return (np.array(data), np.array(header))
+
+def calc_acc(y, y_hat):
+    idx = np.where(y_hat == 1)
+    TP = np.sum(y_hat[idx] == y[idx])
+    idx = np.where(y_hat == -1)
+    TN = np.sum(y_hat[idx] == y[idx])
+    return float(TP + TN)/len(y)
+
+def calc_mse(y, y_hat):
+    return np.nanmean(((y - y_hat) ** 2))
+
+def test_main(filename='data/iris-virginica.txt', C=1.0, kernel_type='linear', epsilon=0.001):
+    # Load data
+    (data, _) = readData('%s/%s' % (filepath, filename), header=False)
+    data = data.astype(float)
+
+    # Split data
+    X, y = data[:,0:-1], data[:,-1].astype(int)
+
+    # Initialize model
+    model = SVCSMO()
+
+    # Fit model
+    support_vectors, iterations = model.fit(X, y)
+
+    # Support vector count
+    sv_count = support_vectors.shape[0]
+
+    # Make prediction
+    y_hat = model.predict(X)
+
+    # Calculate accuracy
+    acc = calc_acc(y, y_hat)
+    mse = calc_mse(y, y_hat)
+
+    print("Support vector count: %d" % (sv_count))
+    print("bias:\t\t%.3f" % (model.b))
+    print("w:\t\t" + str(model.w))
+    print("accuracy:\t%.3f" % (acc))
+    print("mse:\t%.3f" % (mse))
+    print("Converged after %d iterations" % (iterations))
+
+if __name__ == '__main__':
+    param = {}
+    param['filename'] = './small_data/iris-slwc.txt'
+    param['C'] = 0.1
+    param['kernel_type'] = 'linear'
+    param['epsilon'] = 0.001
+
+
+    test_main(**param)
+