def plot(data, weights):
data_mat = array(df['density', 'radio_suger'].values[:,:])
label_mat = mat(df['label'].values[:]).transpose()
m = shape(data_mat)[0]
xcord1 = []
ycord1 = []
xcord2 = []
ycord2 = []
for i in xrange(m):
if label_mat[i] == 1:
xcord1.append(data_mat[i])
ycore1.append(label_mat[i])
else:
xcord2.append(data_mat[i])
ycord2.append(label_mat[i])
plt.figure(1)
ax = plt.subplot(111)
ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
ax.scatter(xcord2, ycord2, s=30, c='greeen')
x = arange(-0.2, 0.8, 1)
y = array((-w[0,0]*x)/w[0,1])
print shape(x)
print shape(y)
plt.sca(ax)
plt.plot(x,y)
plt.xlabel('density')
plt.ylabel('radio_suger')
plt.title('LDA')
plt.show()
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()
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]
import pandas as pd
credit = pd.read_csv("default of credit card clients.csv", header=1)
# Inspect the Dataset
credit.head()
credit.describe()
credit.info()
A = credit.dtypes.index
print(A)
# Visualizations
import matplot.pyplot as plt
import seaborn as sns
plt.hist(credit["LIMIT_BAL"], bins=4)
plt.show
plt.plot(credit["LIMIT_BAL"])
plt.show
X = credit["PAY_0"]
Y = credit["PAY_1"]
plt.scatter(X, Y)
plt.show
# Outlier Detection
B = credit["BILL_AMT1"]
plt.boxplot(A, 0, 'gd')
plot.show
# Correlation Matrix
corrMat = credit.corr()
print(corrMat)
import numpy as np import matplot.pyplot as plt x = linspace(0, 1, 100) y = sin(6 * np.pi * y) ffty = np.fft(y) plt.plot(x, y) 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()
"""import ast
code = (1, 2, ('foo': 'bar'}))
object = ast.literal_eval(code)
print(object)
type(object)"""
# Data Visualization with Python.
import numpy as np
import seaborn as sns
data = np.random.randn(1000)
sns.distplot(data, kde=True, rug=True)
import matplot.pyplot as plt
x = [0, 1, 2, 3, 4, 5, 6]
y = [i**2 for i in x]
plt.scatter(x, y, c='blue', marker='x', s=100)
plt.plot(x, y, color='red', linewidth=2)
plt.xlabel('x data')
plt.ylabel('y data')
plt.title('An example plot')
plt.show()
# MayaVI
from numpy import sin, cos, mgrid, pi, sqrt
from mayavi import mlab
mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
u, v = mgrid[-0.01, -0.035:pi:0.01]
x = 2 / 3. * (cos(u) * cos(2 * v) + sqrt(2) * sin(u) * cos(v)) * cos(u) / (
sqrt(2) - sin(2 * u) * sin(3 * v))
y = 2 / 3. * (cos(u) * sin(2 * v) - sqrt(2) * sin(u) * sin(v)) * cos(u) / (
sqrt(2) - sin(2 * u) * sin(3 * v))
Z = -sqrt(2) * cos(u) * cos(u) / (sqrt(2) - sin(2 * u) * sin(3 * v))