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 visualize_data():
""""""
df = pd.read_csv('sp500_joined_closes.csv')
df_corr = df.corr()
data = df_corr.values
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1) # An one by one plot.
heatmap = ax.pcolor(data, cmap = plt.cm.RdYlGn)
fig.colorbar(heatmap)
ax.set_xticks(np.arange(data.shape[0])+ 0.5, minor=False)
ax.set_yticks(np.arange(data.shape[1])+0.5, minor=False)
ax.inverst_yaxis()
ax.xaxis.tick_top()
column_labels = df_corr.columns
row_labels = df_corr.index
ax.set_xticklables(column_labels)
ax.set_yticklabels(row_labels)
plt.xticks(rotation=90)
heatmap.set_clim(-1, 1)
plt.tight_layout()
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]
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()
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()
verts = []
for row in csv_reader:
verts.append(row)
if float(row[0]) > bigx:
bigx = float(row[0]
if float(row[1]) > bigy:
bigy = float(row[1]
if float(row[0]) > smallx:
smallx = float(row[0]
if float(row[1]) > smally:
smally = float(row[1]
verts.sort()
x_arr = []
y_arr = []
for vert in verts:
x_arr.append(vert[0])
y_arr.append(vert[1])
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8)
ax.set_xlabel('x data')
ax.set_ylabel('y data')
ax.set_xlim(smallx,bigx)
ax.set_ylim(smally,bigy)
ax.plot(x_arr,y_arr,color='blue',lw=2)
plt.show()
fig.savefig('test.png')