def __init__(self, num_features, top_k_size):
self.D = num_features
self.learning_rate = 5e-1
self.cms = CustomCountMinSketch(2, (1 << 15) - 1)
# self.cms = CustomCountMinSketch(2, (1<<15) - 1)
self.top_k = TopK(top_k_size)
self.loss_val = 0
def __init__(self,
sparsity,
cms_type,
hash_func_counts,
batch_size,
count_sketch_size,
top_k,
dataset_dict,
top_k_dict={}):
random.seed(42)
self.learning_rate = 0.5
self.cms = self.cms_dicts[cms_type](hash_func_counts,
count_sketch_size)
self.top_k = TopK(top_k)
self.top_k_dict = top_k_dict
self.load_dataset(dataset_dict)
self.batch_size = batch_size
self.sparsity = sparsity
self.recovered_weight = np.zeros(self.features, )
self.non_zero_indexes = np.nonzero(self.weight)
print("non zero indexes of weights {}".format(self.non_zero_indexes))
self.non_zero_weights = []
for index in self.non_zero_indexes:
self.non_zero_weights.append(self.weight[index])
print("non zero weights {}".format(self.non_zero_weights))
self.loss_val = 0
self.correctly_classified = 0
def __init__(self, num_features):
self.D = num_features
self.learning_rate = 5e-1
self.cms = CountSketch(3, (1 << 18) - 1)
# self.cms = CountSketch(3, int(np.log(self.D) ** 2 / 3))
self.top_k = TopK((1 << 14) - 1)
self.loss_val = 0
def __init__(self, num_features, top_k_size, learning_rate):
self.D = num_features
self.w = np.array([0] * self.D)
self.b = 0
self.learning_rate = learning_rate
self.cms = CustomCountMinSketch(2, (1 << 15) - 1)
self.top_k = TopK(top_k_size)
self.loss_val = 0
def __init__(self,
cms_type,
hash_func_counts,
count_sketch_size,
top_k,
top_k_dict={}):
self.learning_rate = 0.5
self.cms = self.cms_dicts[cms_type](hash_func_counts,
count_sketch_size)
self.top_k = TopK(top_k)
self.top_k_dict = top_k_dict
def __init__(self, num_features):
self.D = num_features
self.learning_rate = 0.5
# self.cms = CustomCountSketch(3, int(np.log(self.D) ** 2 / 3))
self.cms = CustomCountSketch(3, (1 << 18) - 1)
self.top_k = TopK(1 << 14 - 1)
def __init__(self, examples, features, sparsity, dataset_files_path):
super(LogisticRegressionWithHeap,
self).__init__(examples, features, sparsity, dataset_files_path)
self.top_k = TopK(sparsity)
def __init__(self, top_k_size):
self.learning_rate = 0.2
self.cms = CountSketch(3, 1000)
# self.cms = CountSketch(3, int(np.log(self.D) ** 2 / 3))
self.top_k = TopK(top_k_size)
self.loss_val = 0
def __init__(self, count_sketch_size, top_k, feature_size):
self.learning_rate = 0.5
self.cms = CustomCountMinSketch(2, count_sketch_size)
self.top_k = TopK(top_k)
self.recovered_weight_vector = [0] * feature_size
def __init__(self, dimensions, train_file, test_file, size_topK):
super(LogisticRegressionWithHeap,
self).__init__(dimensions, train_file, test_file)
self.top_k = TopK(size_topK)
self.top_k_dict = {}
def __init__(self, num_features):
self.learning_rate = 5e-1
self.cms = CustomCountSketch(3, (1 << 18) - 1)
self.top_k = TopK(num_features)
self.loss_val = 0
