import sys
import disco
 
def estimate_map(e, params):
  x=map(float,e[1].split(' '))
  y=x[params.y_id]
  del x[params.y_id]
  if params.w!=None and y*sum([x[i]*params.w[i] for i in range(len(params.w))])>0: return []
  return [('',[y*a for a in x])]
 
 
def estimate_combiner(k, v, w, done, params):
  if done: 
    if w=={}: return []
    else: return [('', ' '.join(map(repr,w[''])))]
 
  if w=={}: w['']=v
  else: w['']=[w[''][i]+v[i] for i in range(len(v))]
 
 
def estimate_reduce(iter, out, params):
        w=None
        for key,value in iter:
                v=map(float,value.split(' '))
                if w==None: w=[params.learning_rate*a for a in v]
                else: w=[w[i]+params.learning_rate*v[i] for i in range(len(v))]
 
  if w!=None: out.add('', ' '.join(map(repr,w)))
 
 
def predict_map(e, params):
  x=map(float,e[1].split(' '))
  del x[params.y_id]
  return [(e[0],sum([x[i]*params.w[i] for i in range(len(params.w))]))]
 
 
def estimate(input, y_id, w=None, learning_rate=1.0, iterations=10, host="disco://localhost", map_reader=disco.chain_reader):
  for i in range(iterations):
    results = disco.job(host, name = 'perceptron_estimate_' + str(i),
            input_files = input, 
            map_reader = map_reader, 
            fun_map = estimate_map,
            combiner = estimate_combiner,
            reduce = estimate_reduce,
            params = disco.Params(w = w, learning_rate=learning_rate,y_id=y_id),
            sort = False, clean = True)
 
    for key,value in disco.result_iterator(results):
      v=map(float,value.split(' '))
      if w==None: w=v
      else: w=[w[i]+v[i] for i in range(len(w))]
 
    print >>sys.stderr,w
 
  return w
 
 
def predict(input, y_id, w, host="disco://localhost", map_reader=disco.chain_reader):
  results = disco.job(host, name = 'perceptron_predict',
          input_files = input, 
          map_reader = map_reader, 
          fun_map = predict_map,
          params=disco.Params(w=w, y_id=y_id),
          sort = False, clean = False)
 
  return results