diff --git a/RidgeRegression/RidgeRegressionMapReduce.py b/RidgeRegression/RidgeRegressionMapReduce.py
index 00e2b43..25ed5ee 100644
--- a/RidgeRegression/RidgeRegressionMapReduce.py
+++ b/RidgeRegression/RidgeRegressionMapReduce.py
@@ -18,22 +18,77 @@ def cholesky_solution_least_squares(part_one, part_two):
     z     = np.linalg.solve(R,part_two)
     theta = np.linalg.solve(np.transpose(R),z)
     return theta
-
-
+    
+    
+class PrioritySampler(object):
+    
+    def __init__(self,sample_size):
+        self.sample_size = sample_size
+        self.sample      = []
+        
+    def process_observation(self,observation):
+        if len(self.sample) < self.sample_size:
+            self.sample.append(observation)
+            if len(self.sample) == self.sample_size:
+                heapq.heapify(self.sample_cv)
+        else:
+            if observation[0] > self.sample[0][0]:
+                heapq.heapreplace(self.sample,observation)
+                
+    def process_observations(self,observations):
+        for observation in observations:
+            self.process_observation(observation)
+            
+            
 
 class RidgeRegressionHoldOutCV(object):
     
-    def __init__(self,lambdas):
+    def __init__(self,lambdas, data):
         self.lambdas = lambdas
-        self.data = []
-
+        self.data    = data
     
-    def add_mapper_output(self,data):
-        pass
     
+    def run_ridge_regression(self, lambda_ridge , scaling = None):
+        
+        def scaler(x, column_scaler):
+            m = np.shape(x)[1]
+            for i in range(m):
+                x[:,i] = column_scaler(x[:,i])
+            return x
+            
+        X,Y  = [],[]
+        for observation in self.data:
+            features , y = observation[1:]
+            X.append(features)
+            Y.append(y)
+        X = np.array(X)
+        Y = np.array(Y)
+        if scaling == "max-min":
+            X = scaler(X,lambda x: x/(np.max(x) - np.min(x)))
+        elif scaling == "z-score":
+            X = scaler(X,lambda x: (x - np.mean(x))/np.std(x))
+        # scale y to account for bias term
+        Y = Y - np.mean(Y)
+        # in case of max-min and no scaling, we need to substract mean from features
+        if scaling != "z-score":
+            X = scaler(X, lambda x: x-np.mean(x))
+        
+    def cv(self, scaling = None):
+        err = [ self.run_ridge_regression(lambda_ridge, scaling) for lambda_ridge in self.lambdas]
+        lambda_best, err = min([ (self.lambdas[i],err[i]) for i in range(len(self.lambdas)) ], key = lambda t: t[1])
+        return lambda_best
+            
+            
+            
+class DimensionMismatch(Exception):
     
-    def cv(self):
-        pass
+    def __init__(self,expected,observed):
+        self.exp      = expected
+        self.obs      = observed
+        
+    def __str__(self):
+        err = "Expected number of observations: "+self.exp+" , observed: "+self.obs
+        return err
 
 
 
@@ -52,11 +107,12 @@ class RidgeRegression(MRJob):
     Options:
     -----------
     
-          -- dimension  - (int)  number of explanatory variables
-          -- bias       - (bool) if True regression wil include bias term
-    
-
-    
+          --dimension              - (int)  number of explanatory variables
+          --scaling                - (str)  'z-score' or 'max-min'
+          --hold-out-sample-size   - (int)  size of hold out cross validation set 
+          --cv-lambdas             - (str)  name of file containing set of regularisation 
+                                            parameters for cross validation
+                                            
     '''
     
     INPUT_PROTOCOL = RawValueProtocol
@@ -76,7 +132,7 @@ def __init__(self,*args,**kwargs):
         self.x_t_y = [0]*self.dim
         self.n = 0
         self.lambdas_cv = self.read_lambdas(self.options.cv_lambdas)
-        self.sample_cv = []
+        self.sampler = Sampler(self.cv_size)
         
     #------------------------------------------- load & configure options ---------------------------------------#
         
@@ -192,13 +248,8 @@ def mapper_ridge(self,_,line):
         # make sample for hold out cross validation set
         rand_priority = random.randrange(start = 0, stop = 100000000)
         observation = (rand_priority,features,y)
-        if len(self.sample_cv) < self.cv_size:
-            self.sample_cv.append(observation)
-            if len(self.sample_cv) == self.cv_size:
-                heapq.heapify(self.sample_cv)
-        else:
-            if observation[0] > self.sample_cv[0][0]:
-                heapq.heapreplace(self.sample_cv,observation)
+        self.sampler.process_observation(observation)
+        
                 
                 
     def mapper_ridge_final(self):
@@ -207,18 +258,21 @@ def mapper_ridge_final(self):
                               "x_"
                               "x_t_x": x_t_x,
                               "y_av":  self.y_av,
-                              "n":     self.n}
+                              "n":     self.n
+                             }
         if self.options.scaling == "max-min":
             intermediate_stats["max"] = self.max
             intermediate_stats["min"] = self.min
-        
         yield None, ("stats",intermediate_stats)
-        yield None, ("hold_out_cv",self.sample_cv)
+        yield None, ("hold_out_cv",self.sampler.sample)
             
                   
                   
     def reducer_ridge(self, key, vals):
-        ridge = RidgeRegressionHoldOutCV(self.lambdas)
+        '''
+        
+        '''
+        sampler = Sampler(self.cv_size)
         final_summary_stats = {"mu":      [0]*self.dim,
                                "x_t_x":   [[0]*self.dim for i in range(self.dim)],
                                "x_t_y":   [0]*self.dim,
@@ -229,9 +283,10 @@ def reducer_ridge(self, key, vals):
                 mapper_summary = val[1]
                 final_summary_stats = self.join_mapper_intermediate_stats(final_summary_stats,mapper_summary)
             else:
-                ridge.add_mapper_output(val[1])
+                sampler.process_observations(val[1])
         # for each scaling type use cross validation to verify best lambda
         # then use it on all data (including cv set) to find parameters
+        ridge   = RidgeRegressionHoldOutCV(self.lambdas, sampler.sample)
         best_lambda = ridge.cv(self.options.scaling)
         yield None, self.estimate_params(final_summary_stats,best_lambda,self.options.scaling)