/normalizations.py (a959a8cc528f486a80a84e2ab233457870d255a1) (1665 bytes) (mode 100644) (type blob)

#normalizations.py
def min_max(self,metric_name):
  #perform min max normalization of specified metric for all nodes
  #min_max normalization
  #get min and max from redis
  x_min = self.redis.zrange(self.metric_prefix+metric_name, 0, 0, withscores=True, score_cast_func=float)[0][1]
  x_max = self.redis.zrange(self.metric_prefix+metric_name, -1, -1, withscores=True, score_cast_func=float)[0][1]
  
  #print x_min
  #print x_max
  
  for node in self.nodes:
    if x_min == x_max:
      x_normalized = 1.0
    else:
      x = float(self.redis.hget(self.node_prefix+str(node), metric_name))
      x_normalized = (x - x_min) / (x_max - x_min)     
  
    #store value for node and metric
    self.redis.zadd(self.metric_prefix+metric_name+self.normalization_suffix, x_normalized, str(node))
    self.redis.hset(self.node_prefix+str(node),metric_name+self.normalization_suffix, x_normalized)

#max min normalization
def max_min(self,metric_name):
  x_min = self.redis.zrange(self.metric_prefix+metric_name, 0, 0, withscores=True, score_cast_func=float)[0][1]
  x_max = self.redis.zrange(self.metric_prefix+metric_name, -1, -1, withscores=True, score_cast_func=float)[0][1]
  
  for node in self.nodes:
    if x_min == x_max:
      x_normalized = 1.0
    else:
      x = float(self.redis.hget(self.node_prefix+str(node), metric_name))
      x_normalized = (x_max - x) / (x_max - x_min)     

    #store value for node and metric
    self.redis.zadd(self.metric_prefix+metric_name+self.normalization_suffix, x_normalized, str(node))
    self.redis.hset(self.node_prefix+str(node),metric_name+self.normalization_suffix, x_normalized)

Mode Type Size Ref File
100644 blob 6 0d20b6487c61e7d1bde93acf4a14b7a89083a16d .gitignore
100644 blob 103 924a1df9f7338af770d3cf3d4b0ce2673f10d1b0 README.md
100644 blob 0 e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 __init__.py
100644 blob 1256 489636a0073e3dfe2bfd04ee893d609d304a8490 advancedscores.py
100644 blob 4841 c18bb46d148ccb4e04370864e7352cae635f865d config.py
040000 tree - 1eae5e19b1eff05e464e361e3f50f3df23f1b754 data
100644 blob 4728 68b7ae967b6cb349e54a32d6d00528321f1825b3 file_importer.py
100644 blob 2132 b09bec79c503afa1126d86335d1372443e207773 graph tool test.py
100644 blob 716 359eb7179fa58d67044228556f7d9c38b5caec85 indexing.py
100644 blob 33232205 7ca601d1ca32abf3244359ce3ad85ea6a1b60010 log
100644 blob 6361 888c63bb17693deb49d9ae367ed7a3b9cdbeebe0 metric_calculator.py
100644 blob 8308 6673fc89a71f9a87f57997fbd96c7ed3c36fb7ff metrics.py
100644 blob 1665 a959a8cc528f486a80a84e2ab233457870d255a1 normalizations.py
100644 blob 1575 7a6cc1ce0ca8ab13c12325ce4ac45044544ed9a1 pearson.py
100644 blob 1696 26df05e3ec9f549013f400a6f5f5df7fdb617c2e start.py
100644 blob 2144 fb03eaa1cd8eb0d6c17b2019fe4c877a32bb7059 statistics.py
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