diff --git a/example/minecraft.yaml b/example/minecraft.yaml
new file mode 100644
index 00000000..310e5fcb
--- /dev/null
+++ b/example/minecraft.yaml
@@ -0,0 +1,12 @@
+metrics:  [SNR_3x2]
+bands: riz
+training_file: data_buzzard/training.hdf5
+validation_file: data_buzzard/validation.hdf5
+output_file: example/minecraft_output.txt
+
+run:
+  MineCraft:
+    run_0:
+      blocks: 62
+      zbins: 11
+        
diff --git a/tomo_challenge/classifiers/minecraft.py b/tomo_challenge/classifiers/minecraft.py
new file mode 100644
index 00000000..a2aa89bb
--- /dev/null
+++ b/tomo_challenge/classifiers/minecraft.py
@@ -0,0 +1,159 @@
+"""
+Trivial classifiers. Used as example and testing.
+
+Every classifier module needs to:
+ - have construction of the type 
+       __init__ (self, bands, options) (see examples below)
+ -  implement two functions: 
+        train (self, training_data,training_z)
+        apply (self, data).
+ - define valid_options class varible.
+
+See Classifier Documentation below.
+
+
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from .base import Tomographer
+
+class MineCraft(Tomographer):
+    """Completely random classifier. 
+
+    Every object goes into a random bin. 
+    """
+
+    ## see constructor below
+    valid_options = ['blocks','zbins']
+    
+    def __init__ (self, bands, options):
+        """Constructor
+        
+        Parameters:
+        -----------
+        bands: str
+          string containg valid bands, like 'riz' or 'griz'
+        options: dict
+          options come through here. Valid keys are listed as valid_options
+          class variable. 
+
+        Note:
+        -----
+        Valiad options are:
+            'bins' - number of blocks per in direction
+
+        """
+        self.opt = {'blocks':40, 'zbins':10}
+        self.bands = bands
+        self.Nb = len(bands)
+        self.opt.update(options)
+
+
+    def train (self,training_data, training_z):
+        """Trains the classifier
+        
+        Parameters:
+        -----------
+        training_data: numpy array, size Ngalaxes x Nbands
+          training data, each row is a galaxy, each column is a band as per
+          band defined above
+        training_z: numpy array, size Ngalaxies
+          true redshift for the training sample
+
+        """
+        #data = np.vstack([training_data[band] for band in self.bands]).T
+        bins=[]
+        self.bmin,self.bmax = [],[]
+        for band in self.bands:
+            da=training_data[band]
+            mn,mx  = da.min(), da.max()*1.00001
+            self.bmin.append(mn)
+            self.bmax.append(mx)
+            step = (mx-mn)/self.opt['blocks']
+            bins.append(((da-mn)/step).astype(int))
+        bins = np.array(bins).T
+        print (bins[:4])
+        zmin,zmax = training_z.min(), training_z.max()
+        dz= (zmax-zmin)/self.opt['zbins']
+        self.target = {}
+        for i in range(self.opt['zbins']):
+            czmin = zmin+i*dz
+            czmax = czmin+dz
+            print (czmin,czmax)
+            w = np.where((training_z>czmin) & (training_z<=czmax))
+            print (bins.shape)
+            xbins = bins[w[0],:]
+            xbins = set([tuple(b) for b in xbins])
+            ## now that we have unique elements, let's add
+            for bin in xbins:
+                if bin in self.target:
+                    self.target[bin].append(i)
+                else:
+                    self.target[bin] = [i]
+        ## now reorganize
+        for key in self.target.keys():
+            self.target[key] = tuple(self.target[key])#[# key])
+            # if len(l)<6:
+            #     self.target[key] = tuple(self.target[key])
+            # else:
+            #     self.target[key] = ()
+        self.idmap={}
+        for binid,vals in enumerate(set(self.target.values())):
+            self.idmap[vals]=binid
+            print ('id = ',binid,':',vals)
+        self.idmap[()] = len(self.target.keys())  #last +1
+        print (self.idmap)
+
+        if False:
+            bincount=self.apply(training_data)
+            for i in range(bincount.max()):
+                a,b=np.histogram (training_z[bincount==i],bins=30)
+                b=0.5*(b[:1]+b[:-1])
+                plt.plot(b,a)
+            plt.xlabel('z')
+            plt.ylabel('prob')
+            plt.show()
+        
+        #stop()
+        
+        
+    def apply (self,data):
+        """Applies training to the data.
+        
+        Parameters:
+        -----------
+        Data: numpy array, size Ngalaxes x Nbands
+          testing data, each row is a galaxy, each column is a band as per
+          band defined above
+
+        Returns: 
+        tomographic_selections: numpy array, int, size Ngalaxies
+          tomographic selection for galaxies return as bin number for 
+          each galaxy.
+        """
+        bins=[]
+        for band,mn,mx in zip(self.bands,self.bmin,self.bmax):
+            da=data[band]
+            step = (mx-mn)/self.opt['blocks']
+            bins.append(((da-mn)/step).astype(int))
+        bins = [tuple(b) for b in np.array(bins).T]
+        print (bins[:4])
+        tomo_bin = np.array([self.idmap[dict.get(self.target,b,())] for b in bins])
+        No = len(tomo_bin)
+        binc=np.bincount(tomo_bin)
+        print ('bincount before=',binc/No)
+        minobj = int(No*0.03)
+        ## now combine all the samples that are less than 3%:
+        w=np.where(binc<minobj)[0]
+        for j in w[1:]:
+            tomo_bin [tomo_bin==j] = w[0]
+        ## now reindex, again, to get rid of intermediate zeros:
+        for i,j in enumerate(sorted(set(tomo_bin))):
+            if (i!=j):
+                tomo_bin[tomo_bin==j]=i
+            
+        binc=np.bincount(tomo_bin)
+        ## now get rid of zeros:
+        
+        print ('bincount after=',binc/No)
+        return tomo_bin