def mergeArrays2(firstVector, secondVector, length):
if length == 0:
length = firstVector.size
results = normalize_array(firstVector, 0)
firstNumStates = results[0]
firstNormalisedVector = results[1]
results = normalize_array(secondVector, 0)
secondNumStates = results[0]
secondNormalisedVector = results[1]
stateCount = 1
stateMap = np.zeros(shape=(firstNumStates * secondNumStates, ))
merge = np.zeros(shape=(length, ))
for i in range(0, length):
curIndex = firstNormalisedVector[i] + (secondNormalisedVector[i] *
firstNumStates)
if stateMap[int(curIndex)] == 0:
stateMap[int(curIndex)] = stateCount
stateCount = stateCount + 1
merge[i] = stateMap[int(curIndex)]
results = []
results.append(stateCount)
results.append(merge)
return results
def joint_probability(firstVector, secondVector, length):
if length == 0:
length = firstVector.size
results = normalize_array(firstVector, 0)
firstNumStates = results[0]
firstNormalisedVector = results[1]
results = normalize_array(secondVector, 0)
secondNumStates = results[0]
secondNormalisedVector = results[1]
jointNumStates = firstNumStates * secondNumStates
firstStateProbs = find_probs(firstNormalisedVector)
secondStateProbs = find_probs(secondNormalisedVector)
jointStateProbs = np.zeros(shape=(jointNumStates, ))
# Joint probabilities
jointStates = np.column_stack(
(firstNormalisedVector, secondNormalisedVector))
jointIndices, jointCounts = np.unique(jointStates,
axis=0,
return_counts=True)
jointIndices = jointIndices.T
jointIndices = jointIndices[1] * firstNumStates + jointIndices[0]
jointIndices = jointIndices.astype(int)
jointStateProbs[jointIndices] = jointCounts
jointStateProbs /= length
results = []
results.append(jointStateProbs)
results.append(jointNumStates)
results.append(firstStateProbs)
results.append(firstNumStates)
results.append(secondStateProbs)
results.append(secondNumStates)
return results
def mergeArrays(firstVector, secondVector, length):
if length == 0:
length = firstVector.size
results = normalize_array(firstVector, 0)
firstNumStates = results[0]
firstNormalisedVector = results[1]
results = normalize_array(secondVector, 0)
secondNumStates = results[0]
secondNormalisedVector = results[1]
stateCount = 1
stateMap = np.zeros(shape=(firstNumStates * secondNumStates, ))
merge = np.zeros(shape=(length, ))
joint_states = np.column_stack((firstVector, secondVector))
uniques, merge = np.unique(joint_states, axis=0, return_inverse=True)
stateCount = len(uniques)
results = []
results.append(stateCount)
results.append(merge)
return results