import numpy as np
import _pickle as cPickle
import traceback
 
#--------------------    
class storage_layer:
    def __init__(self, inputshape = (1,1,1)):
        
        self.inputshape  = inputshape
        self.outputshape = inputshape
        
        self.mean   = np.zeros(inputshape  + (8,))
        self.var    = np.zeros(inputshape  + (8,8))
        
        self.count = 0
        
    def update_datum(self,z):
        
        self.mean = (self.count*self.mean + z)/(self.count+1)
      
        delta = z - self.mean
        for i in range(8):
            for j in range(8):
                self.var[:,:,:,i,j] = (self.count*self.var[:,:,:,i,j] + delta[:,:,:,i]*delta[:,:,:,j])/(self.count+1)
        
        self.count+=1
        
        return 0
    
#--------------------     
class base_storage_layer:
    def __init__(self, inputshape = (1,1,1)):
        
        self.inputshape  = inputshape
        self.outputshape = inputshape
        
        self.mean   = np.zeros(inputshape)
        self.var    = np.zeros(inputshape)
        
        self.count = 0
        
    def update_datum(self,z):
        
        self.mean = (self.count*self.mean + z)/(self.count+1)
      
        delta = z - self.mean
    
        self.var = (self.count*self.var + delta*delta)/(self.count+1)
        
        self.count+=1
        
        return 0
        
#--------------------    
class trainer:
    def __init__(self, gen_model):
        self.gen_model = gen_model
        self.nlevel = gen_model.nlevel
        self.model = self.construct(nlevel=self.nlevel)
        
        #set name from variable name. http://stackoverflow.com/questions/1690400/getting-an-instance-name-inside-class-init
        (filename,line_number,function_name,text)=traceback.extract_stack()[-2]
        def_name = text[:text.find('=')].strip()
        self.name = def_name
        
        print('Trainer model:',self.name)

        try:
            print('load initial state')
            self.load()
        except:
            print('save initial state')
            self.save()
  
    def construct(self,nlevel=2):
        trainer_model = []
        inputshape = (1,1,1)
        trainer_model.append(base_storage_layer(inputshape))
        for i in np.arange(nlevel):
            trainer_model.append(storage_layer(inputshape))
            inputshape = tuple([(l * 2) for l in inputshape])
        return trainer_model
    
    def transfer(self, silent=False):
        
        if(not silent):
            print('Train model...')
        
        for m, t in zip(self.gen_model.model[::-1][:-1], self.model[::-1][:-1]):
             
            M_z_inv = self.gen_model.get_matrix_inv(t.var)
        
            R_inv     = M_z_inv[:,:,:,0:7,0:7]
            m.Minv    = np.copy(R_inv) 
            R         = self.gen_model.get_matrix_inv(R_inv)
        
            for n in range(7):
                for l in range(7):
                    m.a[:,:,:,n] +=  -M_z_inv[:,:,:,7,l]*R[:,:,:,l,n]
        
            m.w       = self.gen_model.get_matrix_sqrt(R) 
    
            m.b[:,:,:,0]       = t.mean[:,:,:,0] - m.a[:,:,:,0]*t.mean[:,:,:,7]
            m.b[:,:,:,1]       = t.mean[:,:,:,1] - m.a[:,:,:,1]*t.mean[:,:,:,7]
            m.b[:,:,:,2]       = t.mean[:,:,:,2] - m.a[:,:,:,2]*t.mean[:,:,:,7]
            m.b[:,:,:,3]       = t.mean[:,:,:,3] - m.a[:,:,:,3]*t.mean[:,:,:,7]
            m.b[:,:,:,4]       = t.mean[:,:,:,4] - m.a[:,:,:,4]*t.mean[:,:,:,7]
            m.b[:,:,:,5]       = t.mean[:,:,:,5] - m.a[:,:,:,5]*t.mean[:,:,:,7]
            m.b[:,:,:,6]       = t.mean[:,:,:,6] - m.a[:,:,:,6]*t.mean[:,:,:,7]
         
        #update model parameters of base layer
        self.gen_model.model[0].b = self.model[0].mean
        self.gen_model.model[0].w = np.sqrt(self.model[0].var) 
        
        print('test',self.gen_model.model[0].b)
        
        if( not silent):
            print('Training done')
            
        return 0
        
    
    def train_single(self,x_train, silent=False):
        
        assert isinstance(self.gen_model.model, list), "gen_model must be a list"
        assert isinstance(self.model, list), "trainer_model must be a list"
        
        #work through reversed model list and ignore base layer
        if(not silent):
            print('Train model...')
        
        for m, t in zip(self.gen_model.model[::-1][:-1], self.model[::-1][:-1]):
             
            z    = m.kernel.apply(x_train, direction='bwd')
            
            t.update_datum(z)
            
            x_train = z[:,:,:,7]
                 
        self.model[0].update_datum(x_train)
          
        if( not silent):
            print('Training done')
                    
    def save(self):
        """save class as self.name.txt"""
        file = open(self.name+'.txt','wb')
        file.write(cPickle.dumps(self.__dict__))
        file.close()

    def load(self):
        """try load self.name.txt"""
        file = open(self.name+'.txt','rb')
        dataPickle = file.read()
        file.close()

        self.__dict__ = cPickle.loads(dataPickle)