def main():
m = 350
random.seed(2)
X = np.empty([m, 2])
X[:, 0] = np.matrix((random.sample(range(-10000, 10000), m))) / float(1000)
X[:, 1] = np.matrix((random.sample(range(-10000, 10000), m))) / float(1000)
#not separable
y = np.empty([m, 1])
for i in range(X.shape[0]):
y[i] = func2(X[i, :])
#plot data and decision surface
ax = pu.plot_data(X, y)
pu.plot_surface(X, y, X[:, 0], X[:, 1], disc_func=func, ax=ax)
plt.show()
#train svm
#change c to hard/soft margins
w, w0, support_vectors_idx = svm.train(X, y, c=99999, eps=0.1)
#plot result
predicted_labels = svm.classify_all(X, w, w0)
print("Accuracy: {}".format(svm.getAccuracy(y, predicted_labels)))
ax = pu.plot_data(X, y, support_vectors_idx)
pu.plot_surfaceSVM(X[:, 0], X[:, 1], w, w0, ax=ax)
plt.show()
def main():
m=350
random.seed(2)
X = np.empty([m,2])
X[:,0] = np.matrix((random.sample(range(-10000, 10000), m))) / float(1000)
X[:,1] = np.matrix((random.sample(range(-10000, 10000), m))) / float(1000)
#not separable
y = np.empty([m,1])
for i in range(X.shape[0]):
y[i] = func2(X[i,:])
#plot data and decision surface
ax = pu.plot_data(X,y)
pu.plot_surface(X,y, X[:, 0], X[:,1], disc_func=func, ax=ax)
plt.show()
#train svm
#change c to hard/soft margins
w,w0, support_vectors_idx = svm.train(X,y,c=99999,eps=0.1)
#plot result
predicted_labels = svm.classify_all(X,w,w0)
print("Accuracy: {}".format(svm.getAccuracy(y,predicted_labels)))
ax = pu.plot_data(X,y, support_vectors_idx)
pu.plot_surfaceSVM(X[:,0], X[:,1], w,w0, ax=ax)
plt.show()
def main():
data = pandas.read_csv("Data/car.data", sep=",", header=0, index_col=False)
data = pandas.get_dummies(data)
arr = data.as_matrix()
use = [k for k in range(arr.shape[0]) if (arr[k, 0] == -1 or arr[k, 0] == 1)]
arr = arr[use]
X = arr[:, range(1, 22)]
y = arr[:, 0]
# normalize
# X = preprocessing.scale(X)
# shuffle
p = np.random.permutation(len(X))
X = X[p]
y = y[p]
# train svm
w, w0, support_vectors_idx = svm.train(X, y, c=99, eps=0.00001)
# get accuracy
predicted_labels = svm.classify_all(X, w, w0)
print("Accuracy: {}".format(svm.getAccuracy(y, predicted_labels)))
#
# evaluate performance
kfold = svm.kfoldCrossValidation(X, y, 10, 1, c=99, eps=0.00001)
print(kfold)
# evaluate performance with gaussina kernel function
kfold = svm.kfoldCrossValidation(X, y, 10, 1, c=99, eps=0.00001, type="gaussian")
print(kfold)
# evaluate performance with polynomial kernel function
kfold = svm.kfoldCrossValidation(X, y, 10, 1, c=99, eps=0.00001, type="polynomial")
print(kfold)
def main():
dic = {'a':0, 'b':1, '?':-1}
data = np.genfromtxt('Data/credits.data', skip_header=True, delimiter=',',
usecols=[0,1,2,7,10,15],converters={0: lambda s: dic[s]})
use = [k for k in range(len(data)) if data[k][0] != -1 and (not math.isnan(data[k][1]))]
data = data[use]
X = np.empty([len(data),5])
y = np.empty([len(data), 1])
for i in range(len(data)):
for j in range(len(data[i])-1):
X[i,j] = data[i][j]
y[i] = data[i][5]
# preprocessing.scale(X[:,1])
#train svm
w,w0, support_vectors_idx = svm.train(X[:,range(1,5)],y,c=10, eps=1)
#plot result
predicted_labels = svm.classify_all(X[:,range(1,5)],w,w0)
print("Accuracy: {}".format(svm.getAccuracy(y,predicted_labels)))
def main():
m=100
X = np.empty([m,2])
X[:,0] = np.matrix((random.sample(range(-10000, 10000), m))) / float(1000)
X[:,1] = np.matrix((random.sample(range(-10000, 10000), m))) / float(1000)
# preprocessing.scale(X)
#linearly separable
y = np.empty([m,1])
for i in range(m):
y[i] = func(X[i,])
#plot data and decision surface
ax = pu.plot_data(X,y)
pu.plot_surface(X,y, X[:, 0], X[:,1], disc_func=func, ax=ax)
plt.show()
#train svm
w,w0, support_vectors_idx = svm.train(X,y,c=999999999999999, eps=10, type='gaussian')
# w, w0, support_vectors_idx = svm.train(X, y, c=999999999999999, eps=10, type='polynomial')
#plot result
predicted_labels = svm.classify_all(X,w,w0)
print("Accuracy: {}".format(svm.getAccuracy(y,predicted_labels)))
ax = pu.plot_data(X,y, support_vectors_idx)
pu.plot_surfaceSVM(X[:,0], X[:,1], w,w0, ax=ax)
plt.show()
def main():
data = pandas.read_csv('Data/credits.data', sep=',', header=0, index_col=False)
data = pandas.get_dummies(data)
arr = data.as_matrix()
X = arr[:,range(0,6) + range(7,47)]
y = arr[:,6]
# shuffle
p = np.random.permutation(len(X))
X = X[p]
y = y[p]
#train svm
# w,w0, support_vectors_idx = svm.train(X[:,[0,1,2,3,4,5,6,7]],y,c=999, eps=0.000001)
w, w0, support_vectors_idx = svm.train(X, y, c=99999, eps=0.000000001)
#plot result
predicted_labels = svm.classify_all(X,w,w0)
print("Accuracy: {}".format(svm.getAccuracy(y,predicted_labels)))
kfold = svm.kfoldCrossValidation(X, y, 10, 1, c=99, eps=0.00001)
print (kfold)
def main():
m=150
random.seed(2)
X = np.empty([m,2])
X[:,0] = np.matrix((random.sample(range(-10000, 10000), m))) / float(1000)
X[:,1] = np.matrix((random.sample(range(-10000, 10000), m))) / float(1000)
preprocessing.scale(X)
#linearly separable
y = np.empty([m,1])
for i in range(m):
y[i] = func(X[i,])
# shuffle
p = np.random.permutation(len(X))
X = X[p]
y = y[p]
#plot data and decision surface
ax = pu.plot_data(X,y)
pu.plot_surface(X,y, X[:, 0], X[:,1], disc_func=func, ax=ax)
plt.show()
#train svm
w,w0, support_vectors_idx = svm.train(X,y,c=9999, eps=0.000001)
#plot result
predicted_labels = svm.classify_all(X,w,w0)
print("Accuracy: {}".format(svm.getAccuracy(y,predicted_labels)))
kfold = svm.kfoldCrossValidation(X,y,10,1,c=999999999,eps=0.000001)
print (kfold)
ax = pu.plot_data(X,y, support_vectors_idx)
pu.plot_surfaceSVM(X[:,0], X[:,1], w,w0, ax=ax)
plt.show()
def main():
m = 150
random.seed(2)
X = np.empty([m, 2])
X[:, 0] = np.matrix((random.sample(range(-10000, 10000), m))) / float(1000)
X[:, 1] = np.matrix((random.sample(range(-10000, 10000), m))) / float(1000)
preprocessing.scale(X)
#linearly separable
y = np.empty([m, 1])
for i in range(m):
y[i] = func(X[i, ])
# shuffle
p = np.random.permutation(len(X))
X = X[p]
y = y[p]
#plot data and decision surface
ax = pu.plot_data(X, y)
pu.plot_surface(X, y, X[:, 0], X[:, 1], disc_func=func, ax=ax)
plt.show()
#train svm
w, w0, support_vectors_idx = svm.train(X, y, c=9999, eps=0.000001)
#plot result
predicted_labels = svm.classify_all(X, w, w0)
print("Accuracy: {}".format(svm.getAccuracy(y, predicted_labels)))
kfold = svm.kfoldCrossValidation(X, y, 10, 1, c=999999999, eps=0.000001)
print(kfold)
ax = pu.plot_data(X, y, support_vectors_idx)
pu.plot_surfaceSVM(X[:, 0], X[:, 1], w, w0, ax=ax)
plt.show()