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(plot_type):
per_size = 5
nrow, ncol = len(graphs), len(params)
fig = plt.figure(figsize=(ncol * per_size, nrow * per_size))
if plot_type.startswith('dist'):
angle = (10, 45)
else:
angle = (15, 210)
for i, gname in enumerate(graphs):
for j, param in enumerate(params):
idx = i * ncol + j + 1
ax = fig.add_subplot(nrow, ncol, idx, projection='3d')
plot_surface(gname,
param,
plot_type,
fig,
ax=ax,
dirname=dirname,
angle=angle,
use_colorbar=False)
ax.set_title('{}({})'.format(gname, param))
plt.locator_params(axis='y', nbins=5)
plt.locator_params(axis='x', nbins=5)
fig_dir = 'figs/{}'.format(fig_dirname)
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
figpath = '{}/{}.pdf'.format(fig_dir, plot_type)
print(figpath)
fig.savefig(figpath)
def main(plot_type):
per_size = 5
nrow, ncol = len(graphs), len(params)
fig = plt.figure(figsize=(ncol * per_size, nrow * per_size))
if plot_type.startswith('dist'):
angle = (10, 45)
else:
angle = (15, 210)
for i, gname in enumerate(graphs):
for j, param in enumerate(params):
idx = i * ncol + j + 1
ax = fig.add_subplot(nrow, ncol, idx, projection='3d')
plot_surface(gname, param, plot_type,
fig, ax=ax,
dirname=dirname,
angle=angle,
use_colorbar=False)
ax.set_title('{}({})'.format(gname, param))
plt.locator_params(axis='y', nbins=5)
plt.locator_params(axis='x', nbins=5)
fig_dir = 'figs/{}'.format(fig_dirname)
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
figpath = '{}/{}.pdf'.format(fig_dir, plot_type)
print(figpath)
fig.savefig(figpath)
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():
x, y = readData(
"C:/Users/marro/Repo/CS584/Generative_Learning/Data/banknote/data_banknote_authentication.txt",
",",
scale=False)
#shuffle
p = np.random.permutation(len(x))
x = x[p]
y = y[p]
x = x[:, (0, 1)]
# encode class labels
classes, y = np.unique(y, return_inverse=True)
print("Confussion matrix: {}".format(getCM(y, classifyAll(x, x, y))))
print("Training accuracy: {}".format(
getAccuracy(y, classifyAll(x, x, y), 1)))
print("Kfold Accuracy, recall, precission,tp,tn,fp,fn: {}".format(
kfoldCrossValidation(x, y, 10, 1)))
precision = dict()
recall = dict()
average_precision = dict()
for i in range(0, 1):
precision[i], recall[i], _ = precision_recall_curve(
y, classifyAll(x, x, y))
average_precision[i] = average_precision_score(y, classifyAll(x, x, y))
# Plot Precision-Recall curve
plt.clf()
plt.plot(recall[0], precision[0], label='Precision-Recall curve')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall example: AUC={0:0.2f}'.format(
average_precision[0]))
plt.legend(loc="lower left")
plt.show()
# plot data and decision surface
ax = plt.gca()
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
ax.scatter(x[:, 0], x[:, 1], c=(y == 1), cmap=cm_bright)
plt.xlabel("X1")
plt.ylabel("X2")
pu.plot_surface(x, y, x[:, 0], x[:, 1], ax=ax)
plt.show()
def main():
x, y = readData("C:/Users/marro/Repo/CS584/Generative_Learning/Data/banknote/data_banknote_authentication.txt",",",scale=False)
#shuffle
p = np.random.permutation(len(x))
x = x[p]
y = y[p]
x = x[:,(0,1)]
# encode class labels
classes, y = np.unique(y, return_inverse=True)
print("Confussion matrix: {}".format(getCM(y,classifyAll(x,x,y))))
print("Training accuracy: {}".format(getAccuracy(y,classifyAll(x,x,y),1)))
print("Kfold Accuracy, recall, precission,tp,tn,fp,fn: {}".format(kfoldCrossValidation(x,y, 10, 1)))
precision = dict()
recall = dict()
average_precision = dict()
for i in range(0,1):
precision[i], recall[i], _ = precision_recall_curve(y,
classifyAll(x,x,y))
average_precision[i] = average_precision_score(y, classifyAll(x,x,y))
# Plot Precision-Recall curve
plt.clf()
plt.plot(recall[0], precision[0], label='Precision-Recall curve')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall example: AUC={0:0.2f}'.format(average_precision[0]))
plt.legend(loc="lower left")
plt.show()
# plot data and decision surface
ax = plt.gca()
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
ax.scatter(x[:,0], x[:,1], c=(y == 1), cmap=cm_bright)
plt.xlabel("X1")
plt.ylabel("X2")
pu.plot_surface(x,y, x[:, 0], x[:, 1], 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()
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(plot_type):
nrow, ncol = len(dirnames), len(graphs)
per_size = 5
fig = plt.figure(figsize=(per_size * ncol, per_size * nrow))
if plot_type.startswith('dist'):
angle = (10, 45)
elif plot_type.startswith('rank'):
angle = (10, 45)
else:
angle = (15, 210)
for i, (method, dirname) in enumerate(zip(methods, dirnames)):
for j, gname in enumerate(graphs):
idx = i * ncol + j + 1
ax = fig.add_subplot(nrow, ncol, idx, projection='3d')
plot_surface(gname, param, plot_type,
fig, ax=ax,
dirname=dirname,
angle=angle,
use_colorbar=False)
plt.locator_params(axis='y', nbins=5)
plt.locator_params(axis='x', nbins=5)
if i == 0:
ax.set_title(gname)
if j == 0:
ax.set_zlabel(method, size='large')
fig.tight_layout()
fig_dir = 'figs/{}'.format(fig_dirname)
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
figpath = '{}/{}.pdf'.format(fig_dir, plot_type)
print(figpath)
fig.savefig(figpath)
