def StochasticGradientDescent(trainingSet, featureExtractor, lossFunction=None):
# HYPERPARAMETERS: step size (eta), number of iterations (T)
weights = collections.defaultdict(float)
T = 20
for t in range(T):
eta = float(1/float(math.sqrt(t+1)))
for example in trainingSet:
features = featureExtractor(example[0])
loss = 1 - (example[1] * dotProduct(weights, features))
for feature in features:
weights[feature] -= eta*(-features[feature]*example[1]) if (loss >= 1) else 0
return weights
def MinibatchGradientDescent(trainingSet, featureExtractor, lossFunction=None):
# HYPERPARAMETERS: step size (eta), number of iterations (T), number of batches (numBatches)
weights = collections.defaultdict(float)
T = 20
numBatches = 50
for t in range(T):
eta = float(1/float(math.sqrt(t+1)))
batch = random.sample(trainingSet, numBatches)
features = featureExtractor(batch) #TODO: support batch support in your feat. extractor
loss = sum(example * dotProduct(weights, features) for example in batch)
for feature in features:
weights[feature] -= eta*(-features[feature]*example[1]) if (loss >= 1) else 0
return weights
def StochasticGradientDescent(trainingSet,
featureExtractor,
lossFunction=None):
# HYPERPARAMETERS: step size (eta), number of iterations (T)
weights = collections.defaultdict(float)
T = 20
for t in range(T):
eta = float(1 / float(math.sqrt(t + 1)))
for example in trainingSet:
features = featureExtractor(example[0])
loss = 1 - (example[1] * dotProduct(weights, features))
for feature in features:
weights[feature] -= eta * (-features[feature] *
example[1]) if (loss >= 1) else 0
return weights
def MinibatchGradientDescent(trainingSet, featureExtractor, lossFunction=None):
# HYPERPARAMETERS: step size (eta), number of iterations (T), number of batches (numBatches)
weights = collections.defaultdict(float)
T = 20
numBatches = 50
for t in range(T):
eta = float(1 / float(math.sqrt(t + 1)))
batch = random.sample(trainingSet, numBatches)
features = featureExtractor(
batch) #TODO: support batch support in your feat. extractor
loss = sum(example * dotProduct(weights, features)
for example in batch)
for feature in features:
weights[feature] -= eta * (-features[feature] * example[1]) if (
loss >= 1) else 0
return weights
