def plot_multiple(plot_function, args, filename=None, figsize=(14,9), label_left = "Density",
label_bottom="Coarse Grain Measure Value", label_right="Energy",
legend_pos=(1.40,0.90), legend_labels=None):
fig = plt.figure(figsize=figsize)
#figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')
num_structs = len(args)
rows = int(1.5 * math.sqrt(num_structs))
cols = int(math.ceil(num_structs / float(rows)))
ls,lb = None,None
#figsize(cols * 4, rows * 3)
for i, arg in enumerate(args):
ax = fig.add_subplot(rows, cols, i+1)
try:
(ls, lb) = plot_function(arg, ax=ax)
except Exception:
import traceback
print >>sys.stderr, traceback.format_exc()
#print >>sys.stderr, "Error:", str(e)
continue
fig.add_subplot(rows, cols, 1)
ax = fig.add_subplot(rows, cols, num_structs)
#ls1, lb1 = ax1.get_legend_handles_labels()
if label_bottom is not None:
fig.text(0.5, 0.00, label_bottom, ha='center', va='center', fontsize=13)
if label_left is not None:
fig.text(0.00, 0.60, label_left, ha='center', va='center', rotation='vertical', fontsize=13)
if label_right is not None:
fig.text(1., 0.60, label_right, ha='center', va='center', rotation='vertical', fontsize=13)
plt.tight_layout()
plt.subplots_adjust(top=1.30)
if legend_labels is not None:
lb = legend_labels
if ls is not None and lb is not None:
plt.legend(ls, lb, bbox_to_anchor=legend_pos, loc=2, borderaxespad=0.)
if filename is not None:
# blah
plt.savefig(filename, bbox_inches='tight')
return ax
def predict_price(dates,prices,x):
dates = np.reshape(dates,len(dates),1)
svr_len = SVR(kernel='linear',c=1e3)
svr_poly = SVR(kernel='poly',c=1e3,degree=2)
svr_len = SVR(kernel='rbf',c=1e3,gamma=0.1)
svr_lin.fit(dates,prices)
svr_poly.fit(dates,prices)
svr_rbf.fit(dates,prices)
plt.scatter(dates,prices,color='black', label='Data')
plt.plot(dates, svr_rbf.predict(dates), color='red', label='RBF model')
plt.plot(dates, svr_lin.predict(dates), color='green', label='Linear model')
plt.plot(dates, svr_ply.predict(dates), color='blue', label='Ploynomial model')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Support Vector Regration')
plt.legend()
plt.show()
return svr_rbf.predict(x)[0],svr_lin.predict(x)[0], ,svr_poly.predict(x)[0]
def plot_the_loss_curve(epochs, mae_training, mae_validation):
"""Plot a curve of loss vs. epoch."""
plt.figure()
plt.xlabel("Epoch")
plt.ylabel("Root Mean Squared Error")
plt.plot(epochs[1:], mae_training[1:], label="Training Loss")
plt.plot(epochs[1:], mae_validation[1:], label="Validation Loss")
plt.legend()
# We're not going to plot the first epoch, since the loss on the first epoch
# is often substantially greater than the loss for other epochs.
merged_mae_lists = mae_training[1:] + mae_validation[1:]
highest_loss = max(merged_mae_lists)
lowest_loss = min(merged_mae_lists)
delta = highest_loss - lowest_loss
print(delta)
top_of_y_axis = highest_loss + (delta * 0.05)
bottom_of_y_axis = lowest_loss - (delta * 0.05)
plt.ylim([bottom_of_y_axis, top_of_y_axis])
plt.show()
target_size=(150, 150),
batch_size=20,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(validation_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=2e-5),
metrics=['acc'])
history = model.fit_generator(train_generator,
steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=50)
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
import matplot.pyplot as plt from scipy import special import numpy as np ai, aip, bi, bip = special.airy(x) x = linspace(-15, 5, 201) plt.plot(x, ai, 'r', label='Ai(x)') plt.plot(x, bi, 'b--', label='Bi(x)') plt.ylim(-0.5, 1.0) plt.grid plt.legend(loc='upper left') plt.show()
# decision function calculates the raw anomaly score for every point
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) * -1
Z = Z.reshape(xx.shape)
# fill blue map colormap from minimum anomaly score to threshold value
plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 7),cmap=plt.cm.Blues_r)
# draw red contour line where anomaly score is equal to thresold
a = plt.contour(xx, yy, Z, levels=[threshold],linewidths=2, colors='red')
# fill orange contour lines where range of anomaly score is from threshold to maximum anomaly score
plt.contourf(xx, yy, Z, levels=[threshold, Z.max()],colors='orange')
b = plt.scatter(IX1,IX2, c='white',s=20, edgecolor='k')
c = plt.scatter(OX1,OX2, c='black',s=20, edgecolor='k')
plt.axis('tight')
# loc=2 is used for the top left corner
plt.legend(
[a.collections[0], b,c],
['learned decision function', 'inliers','outliers'],
prop=matplotlib.font_manager.FontProperties(size=20),
loc=2)
plt.xlim((0, 1))
plt.ylim((0, 1))
plt.title(clf_name)
plt.show()