def Algorithm(inputData, outputData, outputList, parameters, param):
train_x = inputData[:, 0:4000]
train_y = outputData[:, 0:4000]
test_x = inputData[:, 4000:4600]
test_y = outputData[:, 4000:4600]
test_y_List = outputList
if (param == True):
startTime = time.time()
parameters = L_layer_model(train_x,
train_y,
layers_dims,
num_iterations=10000,
print_cost=True)
endTime = time.time()
t1 = time.time()
pred_test, cost = functions.predict(test_x, test_y, parameters)
t2 = time.time()
avgError = functions.averageError(pred_test, test_y)
outList.append(pred_test[0])
outList.append(test_y[0])
outList.append([cost])
outList.append([avgError])
if (param == True):
outList.append([endTime - startTime])
outList.append([t2 - t1])
costList = []
averageErrorList = []
for j in range(0, np.shape(test_y_List)[0]):
pred_test, cost = functions.predict(test_x, test_y_List[j][:,
4000:4600],
parameters)
test_x = np.concatenate((test_x[1:6, :], pred_test, test_x[6:, :]), 0)
costList.append(cost)
averageError = functions.averageError(pred_test,
test_y_List[j][:, 4000:4600])
averageErrorList.append(averageError)
outList.append(costList)
outList.append(averageErrorList)
def CostList(parameters):
costList = []
averageErrorList = []
for i in range(0 + predictionData.p, 136 - predictionData.p):
X, Y, Y_List = predictionData.DataSet(predictionData.postMile[i])
pred_test, cost = functions.predict(X[:, 4000:4600], Y[:, 4000:4600],
parameters)
averageError = functions.averageError(pred_test, Y[:, 4000:4600])
costList.append(cost)
averageErrorList.append(averageError)
outList.append(costList)
outList.append(averageErrorList)
import functions
test_x = predictionData.inputData[:, 4000:4600]
test_y = predictionData.outputData[:, 4000:4600]
print(np.shape(test_y))
initial_val = test_x[:, 1]
def MovingAverage(inputArray):
#weights = np.array([1.0/21, 2.0/21, 3.0/21, 4.0/21, 5.0/21, 6.0/21])
#inputArray = np.multiply(inputArray, weights)
return np.average(inputArray)
predict_y = []
inputArray = initial_val
print(inputArray)
for i in range(0, np.shape(test_x)[1]):
print(inputArray)
y = MovingAverage(test_x[:, i:i + 1])
#inputArray = np.append(inputArray[1:], y)
predict_y.append(y)
cost = functions.compute_cost(predict_y, test_y)
averageError = functions.averageError(predict_y, test_y)
print(cost)
print(averageError)