def predict(Xnew, var, X, Y):
newinput = Xnew
x = np.delete(X, 0, 0)
y = np.delete(Y, 0, 0)
newinput = np.tile(newinput, (len(x), 1)) # create copies of new input
Xdata = np.vstack((newinput, x))
Ydata = np.tile(y, (2, 1))
result1, result2 = circuit(var, Xdata, Ydata)
return result2*result1
def test(Xtest, Ytest, X, Y, var):
count = 0
x = np.delete(X, 0, 0)
y = np.delete(Y, 0, 0)
Ydata = np.tile(y, (2, 1))
for i in enumerate(Xtest):
idx = i[0]
newinput = i[1]
newinput = np.tile(newinput, (len(x), 1))
Xdata = np.vstack((newinput, x))
result1, result2 = circuit(var, X=Xdata, Y=Ydata)
if np.round(result1*result2)==int(Ytest[idx]):
count += 1
accuracy = count/len(Xtest)
return accuracy
def cost(var, X, Y): # specifically for dataset = 5 points # 4 data points and 1 test input
MSE = 0
for i in enumerate(X):
idx = i[0]
newinput = i[1]
x = np.delete(X, i[0], 0) # remove chosen data point (which is the "new input")
if idx == len(x) - 1: break
newinput = np.tile(newinput, (len(x), 1)) # create copies of new input
Xdatacost = np.vstack((newinput, x)) # stack 4 data points and copies of new input for the circuit
ytrue = int(Y[idx]) # select the true y label of the new input
y = np.delete(Y, idx, 0) # remove chosen data point's label
Ydatacost = np.tile(y, (2, 1)) # Create copy of labels
result1, result2 = circuit(var, X=Xdatacost, Y=Ydatacost)
ypred = result1 * result2
loss = (ypred - ytrue) ** 2
MSE = MSE + loss
return MSE / len(x)
def cost(var, Xdata, Y):
MSE = 0
for i in enumerate(X):
idx = i[0]
newinput = i[1]
x = np.delete(
Xdata, i[0], 0
) # remove chosen data point (which is the "new input") so that only the other 4 data points go into the cost
if idx == len(x) - 1: break
newinput = np.tile(newinput, (len(x), 1)) # create copies of new input
Xdata = np.vstack(
(newinput,
x)) # stack 4 data points and copies of new input for the circuit
ytrue = int(Y[idx]) # select the true y label of the new input
y = np.delete(Y, idx, 0) # remove chosen data point's label
Ydata = np.tile(y, (2, 1)) # Create copy of labels
result1, result2 = variational_circ(var=var, Xdata=Xdata, Y=Ydata)
ypred = result1 * result2
loss = (ypred - ytrue)**2
MSE = MSE + loss
print(MSE)
return MSE / len(x)
def embedding_1(X, wires, fill='redundant'):
if len(X) < len(wires):
r_ = len(wires) // len(X)
if fill == 'redundant':
large_features = np.tile(X, r_)
elif fill == 'pad':
large_features = np.pad(X, (0, len(wires)),
'constant',
constant_values=0)
qml.templates.embeddings.AngleEmbedding(
large_features, wires=wires,
rotation='Y') # replace with more general embedding
else:
qml.templates.embeddings.AngleEmbedding(
X, wires=wires,
rotation='Y') # replace with more general embedding
qml.templates.embeddings.AngleEmbedding(X, wires=wires)
def embedding_1(X, wires, fill='redundant'):
"""
Args:
X:
wires:
fill: (Default value = 'redundant')
Returns:
"""
if len(X) < len(wires):
r_ = len(wires) // len(X)
if fill == 'redundant':
large_features = np.tile(X, r_)
elif fill == 'pad':
large_features = np.pad(X, (0, len(wires)), 'constant', constant_values=0)
qml.templates.embeddings.AngleEmbedding(large_features, wires=wires, rotation='Y')
else:
qml.templates.embeddings.AngleEmbedding(X, wires=wires, rotation='Y')