def setUp(self): # initialize the logger job_logger = logging.getLogger('jobs') job_logger.setLevel(logging.WARNING) formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") ch = logging.StreamHandler(sys.stdout) ch.setFormatter(formatter) job_logger.addHandler(ch) cfg = config.load_config('config.cfg') self.server = server.Server(cfg)
def setUp(self): cfg = config.load_config('config.cfg') self.server = server.Server(cfg)
import numpy import redisml.server.server as server import redisml.server.configuration as config def gnnmf(v, w, h): max_iteration = 10 for i in range(0, max_iteration): # Numpy code: # h = h*(w.transpose().dot(h)/w.transpose().dot(w).dot(h)) # w = w*(v.dot(h.transpose())/w.dot(h).dot(h.transpose())) h = h.cw_multiply(w.multiply(h, transpose_self=True).cw_divide(w.multiply(w, transpose_self=True).multiply(h))) w = w.cw_multiply(v.multiply(h, transpose_m=True).cw_divide(w.multiply(h).multiply(h, transpose_m=True))) cfg = config.load_config('config.cfg') s = server.Server(cfg) # Here we generate random matrices # Use Server.matrix_from_name to load an existing matrix from redis v_ = numpy.random.rand(1024,1024) v = s.matrix_from_numpy(v_) w_ = numpy.random.rand(1024,1024) w = s.matrix_from_numpy(w_) h_ = numpy.random.rand(1024,1024) h = s.matrix_from_numpy(h_) gnnmf(v, w, h) print h
import numpy import redisml.server.server as server import redisml.server.configuration as config def kmeans(data, centers, k): # iterate k-means for i in range(0, k): dist = data.k_means_distance(centers) centers = data.k_means_recalc(dist) # Create a server cfg = config.load_config("config.cfg") s = server.Server(cfg) # Generate random data data_ = numpy.random.rand(1024, 16) # Take first 10 rows as centers centers_ = data_[0:10, :] # Create redisml matrices data = s.matrix_from_numpy(data_) centers = s.matrix_from_numpy(centers_) kmeans(data, centers, 10) print centers