def T12():
'''
Tests saving a model to file
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis=1)**2).reshape(-1, 1)
m1 = MLPR([4, 4, 1], maxIter=16, name='t12ann1')
m1.fit(A, Y)
m1.SaveModel('./t12ann1')
return True
def T1():
'''
Tests basic functionality of MLPR
'''
A = np.random.rand(32, 4)
Y = np.random.rand(32, 1)
a = MLPR([4, 4, 1], maxIter=16, name='mlpr1')
a.fit(A, Y)
a.score(A, Y)
a.predict(A)
return True
def T9():
'''
Tests if multiple MLPRs can be created without affecting each other
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis=1)**2).reshape(-1, 1)
m1 = MLPR([4, 4, 1], maxIter=16)
m1.fit(A, Y)
s1 = m1.score(A, Y)
m2 = MLPR([4, 4, 1], maxIter=16)
m2.fit(A, Y)
s2 = m1.score(A, Y)
if s1 != s2:
return False
return True
def T8():
'''
Tests if multiple MLPRs can be created without affecting each other
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis=1)**2).reshape(-1, 1)
m1 = MLPR([4, 4, 1], maxIter=16)
m1.fit(A, Y)
R1 = m1.GetWeightMatrix(0)
m2 = MLPR([4, 4, 1], maxIter=16)
m2.fit(A, Y)
R2 = m1.GetWeightMatrix(0)
if (R1 != R2).any():
return False
return True
def T14():
'''
Tests saving and restore a model
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis=1)**2).reshape(-1, 1)
m1 = MLPR([4, 4, 1], maxIter=16, name='t12ann1')
m1.fit(A, Y)
m1.SaveModel('./t12ann1')
R1 = m1.GetWeightMatrix(0)
ANN.Reset()
m1 = MLPR([4, 4, 1], maxIter=16, name='t12ann2')
m1.RestoreModel('./', 't12ann1')
R2 = m1.GetWeightMatrix(0)
if (R1 != R2).any():
return False
return True
print(stockData2) print(scale(stockDataASC)) # to get only specific columns of data from a array use : # data[:, [1, 9]] where data is array and you want columns 1, 9 (index start from 0) # scale the stock data, volume to ease the calculations and fit within the data range # Number of neurons in the input layer # 4 neurons to indicate the candle stick doji patterns # 4 neurons to indicate the previous most tested highs which are greater than previous day closing prices # 4 neurons to indicate the previous most tested lows which are less than previous day closing prices # 5 neurons to indicate the previous day open, close, high and low prices, and volume i = 17 # Number of neurons in the output layer # 5 neurons to indicate the current day open, close, high and low prices and volume o = 5 #Number of neurons in the hidden layers h = 17 #The list of layer sizes layers = [i, h, h, h, h, h, h, h, h, h, o] mlpr = MLPR(layers, maxIter=1000, tol=0.40, reg=0.001, verbose=True) mlpr.fit() #Begin prediction yHat = mlpr.predict(A) #Plot the results mpl.plot(A, Y, c='#b0403f') mpl.plot(A, yHat, c='#5aa9ab') mpl.show()
#Number of neurons in the input layer i = 4 #Number of neurons in the output layer o = 4 #Number of neurons in the hidden layers h = 4 #The list of layer sizes layers = [i, h, o] mlpr = MLPR(layers, maxIter = 10000, tol = 0.0010, reg = 0.001, verbose = True) #Length of the hold-out period n = len(A) nDays = int(round(n*.3)) #Learn the data mlpr.fit(A[0: (n - nDays)], Y[1:(n - nDays + 1)]) #Begin prediction yHat = mlpr.predict(A[0: (n - 1)]) #Plot the results # mPlotLib.plot(list(range(nDays - 1)), Y[(n-nDays + 1):, (0)].reshape(-1, 1), c='#b04fff') # mPlotLib.plot(list(range(nDays - 1)), yHat[:, (0)].reshape(-1, 1), c='#000000') # # # mPlotLib.plot(A[(n-nDays): (n-1)], yHat, c='#5aa9ab') # mPlotLib.show() # plot with various axes scales mPlotLib.figure(1) nDays = n # linear