In [41]:
#Imports and model parameters

import tensorflow as tf
import numpy as np
#Simple network: Given three integers a,b,c, [-100,100] chooses three random x-values, and evaluates
#the quadratic function a*x^2 + b*x + c at those values.

import copy

alpha,hidden_dim,hidden_dim2 = (.001,4,4)

thresh = .03

# Parameters
learning_rate = 0.0005
training_epochs = 15
batch_size = 2000
display_step = 1

# Network Parameters
n_hidden_1 = 4 # 1st layer number of features
n_hidden_2 = 4 # 2nd layer number of features
n_input = 1 # Guess quadratic function
n_classes = 1 # 
#synapses = []
models = []

#Testing starting in the same place
#synapse0 = 2*np.random.random((1,hidden_dim)) - 1
#synapse1 = 2*np.random.random((hidden_dim,hidden_dim2)) - 1
#synapse2 = 2*np.random.random((hidden_dim2,1)) - 1
#copy_model = multilayer_perceptron(ind=0)

In [59]:
#Function definitions

def func(x,a,b,c):
    return x*x*a + x*b + c

def generatecandidate4(a,b,c,tot):
    
    candidate = [[np.random.random() for x in xrange(1)] for y in xrange(tot)]
    candidatesolutions = [[func(x[0],a,b,c)] for x in candidate]
    
    return (candidate, candidatesolutions)

def synapse_interpolate(synapse1, synapse2, t):
    return (synapse2-synapse1)*t + synapse1

def model_interpolate(w1,b1,w2,b2,t):
    
    m1w = w1
    m1b = b1
    m2w = w2 
    m2b = b2
    
    mwi = [synapse_interpolate(m1we,m2we,t) for m1we, m2we in zip(m1w,m2w)]
    mbi = [synapse_interpolate(m1be,m2be,t) for m1be, m2be in zip(m1b,m2b)]
    
    return mwi, mbi

def InterpBeadError(w1,b1, w2,b2, write = False, name = "00"):
    errors = []
    
    xdat,ydat = generatecandidate4(.5, .25, .1, 1000)
    xdat = np.array(xdat)
    ydat = np.array(ydat)
    
    
    
    
    for tt in xrange(100):
        #print tt
        #accuracy = 0.
        t = tt/100.
        thiserror = 0

        #x0 = tf.placeholder("float", [None, n_input])
        #y0 = tf.placeholder("float", [None, n_classes])
        weights, biases = model_interpolate(w1,b1,w2,b2, t)
        interp_model = multilayer_perceptron(w=weights, b=biases)
        
        with interp_model.g.as_default():
            
            #interp_model.UpdateWeights(weights, biases)


            x = tf.placeholder("float", [None, n_input])
            y = tf.placeholder("float", [None, n_classes])
            pred = interp_model.predict(x)
            init = tf.initialize_all_variables()


            with tf.Session() as sess:
                sess.run(init)
                correct_prediction = tf.reduce_mean(tf.square(pred-y))
                accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
                print "Accuracy:", accuracy.eval({x: xdat, y: ydat}),"\t",tt,weights[0][0][0],weights[0][0][1]
                thiserror = accuracy.eval({x: xdat, y: ydat})


        errors.append(thiserror)

    if write == True:
        with open("f" + str(name) + ".out",'w+') as f:
            for e in errors:
                f.write(str(e) + "\n")
    
    return max(errors), np.argmax(errors)

In [43]:
#Class definitions

class multilayer_perceptron():
    
    #weights = {}
    #biases = {}
    
    def __init__(self, w=0, b=0, ind='00'):
        
        self.index = ind #used for reading values from file
        #See the filesystem convention below (is this really necessary?)
        #I'm going to eschew writing to file for now because I'll be generating too many files
        #Currently, the last value of the parameters is stored in self.params to be read
        
        learning_rate = 0.01
        training_epochs = 15
        batch_size = 1000
        display_step = 1

        # Network Parameters
        n_hidden_1 = 4 # 1st layer number of features
        n_hidden_2 = 4 # 2nd layer number of features
        n_input = 1 # Guess quadratic function
        n_classes = 1 # 
        self.g = tf.Graph()
        
        
        self.params = []
        
        with self.g.as_default():
        
            #Note that by default, weights and biases will be initialized to random normal dists
            if w==0:
                
                self.weights = {
                    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
                    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
                    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
                }
                self.weightslist = [self.weights['h1'],self.weights['h2'],self.weights['out']]
                self.biases = {
                    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
                    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
                    'out': tf.Variable(tf.random_normal([n_classes]))
                }
                self.biaseslist = [self.biases['b1'],self.biases['b2'],self.biases['out']]
                
            else:
                
                self.weights = {
                    'h1': tf.Variable(w[0]),
                    'h2': tf.Variable(w[1]),
                    'out': tf.Variable(w[2])
                }
                self.weightslist = [self.weights['h1'],self.weights['h2'],self.weights['out']]
                self.biases = {
                    'b1': tf.Variable(b[0]),
                    'b2': tf.Variable(b[1]),
                    'out': tf.Variable(b[2])
                }
                self.biaseslist = [self.biases['b1'],self.biases['b2'],self.biases['out']]
            self.saver = tf.train.Saver()
    
    
    def UpdateWeights(self, w, b):
        with self.g.as_default():
            self.weights = {
                    'h1': tf.Variable(w[0]),
                    'h2': tf.Variable(w[1]),
                    'out': tf.Variable(w[2])
                }
            self.weightslist = [self.weights['h1'],self.weights['h2'],self.weights['out']]
            self.biases = {
                'b1': tf.Variable(b[0]),
                'b2': tf.Variable(b[1]),
                'out': tf.Variable(b[2])
            }
            self.biaseslist = [self.biases['b1'],self.biases['b2'],self.biases['out']]
            

        
    def predict(self, x):
        
        with self.g.as_default():
            layer_1 = tf.add(tf.matmul(x, self.weights['h1']), self.biases['b1'])
            layer_1 = tf.nn.relu(layer_1)
            # Hidden layer with RELU activation
            layer_2 = tf.add(tf.matmul(layer_1, self.weights['h2']), self.biases['b2'])
            layer_2 = tf.nn.relu(layer_2)
            # Output layer with linear activation
            out_layer = tf.matmul(layer_2, self.weights['out']) + self.biases['out']
            return out_layer
        
    def ReturnParamsAsList(self):
        
        with self.g.as_default():

            with tf.Session() as sess:
                # Restore variables from disk
                self.saver.restore(sess, "/home/dfreeman/PythonFun/tmp/model"+str(self.index)+".ckpt")                
                return sess.run(self.weightslist), sess.run(self.biaseslist)

        
        
class WeightString:
    
    def __init__(self, w1, b1, w2, b2, numbeads, threshold):
        self.w1 = w1
        self.w2 = w2
        self.b1 = b1
        self.b2 = b2
        #self.w2, self.b2 = m2.params
        self.AllBeads = []

        self.threshold = threshold
        
        self.AllBeads.append([w1,b1])
        
        
        for n in xrange(numbeads):
            ws,bs = model_interpolate(w1,b1,w2,b2, (n + 1.)/(numbeads+1.))
            self.AllBeads.append([ws,bs])
            
        self.AllBeads.append([w2,b2])
        
        
        self.ConvergedList = [False for f in xrange(len(self.AllBeads))]
        self.ConvergedList[0] = True
        self.ConvergedList[-1] = True
    
    
    def SpringNorm(self, order):
        
        total = 0.
        
        #Energy between mobile beads
        for i,b in enumerate(self.AllBeads):
            if i < len(self.AllBeads)-1:
                #print "Tallying energy between bead " + str(i) + " and bead " + str(i+1)
                subtotal = 0.
                for j in xrange(len(b)):
                    subtotal += np.linalg.norm(np.subtract(self.AllBeads[i][0][j],self.AllBeads[i+1][0][j]),ord=order)#/len(self.beads[0][j])
                for j in xrange(len(b)):
                    subtotal += np.linalg.norm(np.subtract(self.AllBeads[i][1][j],self.AllBeads[i+1][1][j]),ord=order)#/len(self.beads[0][j])
                total+=subtotal
        
        return total#/len(self.beads)
        
    
    
    def SGDBead(self, bead, thresh, maxindex):
        
        finalerror = 0.
        
        #thresh = .05

        # Parameters
        learning_rate = 0.01
        training_epochs = 15
        batch_size = 1000
        display_step = 1
        
        curWeights, curBiases = self.AllBeads[bead]
        test_model = multilayer_perceptron(w=curWeights, b=curBiases)

        with test_model.g.as_default():

            x = tf.placeholder("float", [None, n_input])
            y = tf.placeholder("float", [None, n_classes])
            pred = test_model.predict(x)
            cost = tf.reduce_mean(tf.square(pred-y))
            optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)
            init = tf.initialize_all_variables()
            stopcond = True

            with tf.Session() as sess:
                sess.run(init)
                xtest, ytest = generatecandidate4(.5,.25,.1,1000)
                j = 0
                while stopcond:
                    for epoch in range(training_epochs):
                        avg_cost = 0.
                        total_batch = int(10000/batch_size)
                        if (avg_cost > thresh or avg_cost == 0.) and stopcond:
                        # Loop over all batches
                            for i in range(total_batch):
                                batch_x, batch_y = generatecandidate4(.5,.25,.1,batch_size)
                                # Run optimization op (backprop) and cost op (to get loss value)
                                _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                                              y: batch_y})
                                # Compute average loss
                                avg_cost += c / total_batch
                            # Display logs per epoch step
                            #if epoch % display_step == 0:
                            #    print "Epoch:", '%04d' % (epoch+1), "cost=", \
                            #        "{:.9f}".format(avg_cost)

                            if avg_cost < thresh:
                                stopcond = False
                    #print "Optimization Finished!"

                    # Test model
                    #correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
                    correct_prediction = tf.reduce_mean(tf.square(pred-y))
                    # Calculate accuracy
                    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
                    print "Accuracy:", accuracy.eval({x: xtest, y: ytest})

                    #if (j%5000) == 0:
                    #    print "Error after "+str(j)+" iterations:" + str(accuracy.eval({x: xtest, y: ytest}))

                    finalerror = accuracy.eval({x: xtest, y: ytest})
                    
                    if finalerror < thresh or stopcond==False:# or j > maxindex:
                        #print "Changing stopcond!"
                        stopcond = False
                        #print "Final params:"
                        test_model.params = sess.run(test_model.weightslist), sess.run(test_model.biaseslist)
                        self.AllBeads[bead]=test_model.params
                        print "Final bead error: " + str(finalerror)
                        
                    j+=1

            return finalerror

In [44]:
#Model generation

for ii in xrange(3):

    '''weights = {
        'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
        'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
        'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
    }
    biases = {
        'b1': tf.Variable(tf.random_normal([n_hidden_1])),
        'b2': tf.Variable(tf.random_normal([n_hidden_2])),
        'out': tf.Variable(tf.random_normal([n_classes]))
    }'''

    # Construct model with different initial weights
    test_model = multilayer_perceptron(ind=ii)
    
    #Construct model with same initial weights
    #test_model = copy.copy(copy_model)
    #test_model.index = ii
    
    
    
    
    #print test_model.weights
    

    
    models.append(test_model)
    with test_model.g.as_default():

        x = tf.placeholder("float", [None, n_input])
        y = tf.placeholder("float", [None, n_classes])
        pred = test_model.predict(x)

        # Define loss and optimizer
        #cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
        cost = tf.reduce_mean(tf.square(pred-y))
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)

        # Initializing the variables
        init = tf.initialize_all_variables()


        #remove the comment to get random initialization
        stopcond = True




        with tf.Session() as sess:
            sess.run(init)
            xtest, ytest = generatecandidate4(.5,.25,.1,1000)

            while stopcond:
                #print 'epoch:' + str(e)
                #X = []
                #y = []
                j = 0
                # Training cycle
                for epoch in range(training_epochs):
                    avg_cost = 0.
                    total_batch = int(10000/batch_size)

                    if (avg_cost > thresh or avg_cost == 0.) and stopcond:
                    # Loop over all batches
                        for i in range(total_batch):
                            batch_x, batch_y = generatecandidate4(.5,.25,.1,batch_size)
                            # Run optimization op (backprop) and cost op (to get loss value)
                            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                                          y: batch_y})
                            # Compute average loss
                            avg_cost += c / total_batch
                        # Display logs per epoch step
                        if epoch % display_step == 0:
                            print "Epoch:", '%04d' % (epoch+1), "cost=", \
                                "{:.9f}".format(avg_cost)

                        if avg_cost < thresh:
                            stopcond = False
                            #test_model.params = sess.run(test_model.weightslist), sess.run(test_model.biaseslist)
                            #save_path = test_model.saver.save(sess,"/home/dfreeman/PythonFun/tmp/model" + str(ii) + ".ckpt")
                            
                print "Optimization Finished!"

                # Test model
                #correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
                correct_prediction = tf.reduce_mean(tf.square(pred-y))
                # Calculate accuracy
                accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
                print "Accuracy:", accuracy.eval({x: xtest, y: ytest})

                if (j%5000) == 0:
                    print "Error after "+str(j)+" iterations:" + str(accuracy.eval({x: xtest, y: ytest}))

                if accuracy.eval({x: xtest, y: ytest}) < thresh or stopcond == False:
                    #print "Changing stopcond!"
                    stopcond = False
                    print "Final params:"
                    test_model.params = sess.run(test_model.weightslist), sess.run(test_model.biaseslist)
                    save_path = test_model.saver.save(sess,"/home/dfreeman/PythonFun/tmp/model" + str(ii) + ".ckpt")
                j+=1
    #remove the comment to get random initialization

    
    #synapses.append([synapse_0,synapse_1,synapse_2


Epoch: 0001 cost= 0.268766576
Epoch: 0002 cost= 0.247447196
Epoch: 0003 cost= 0.231380257
Epoch: 0004 cost= 0.215012711
Epoch: 0005 cost= 0.201905853
Epoch: 0006 cost= 0.191673702
Epoch: 0007 cost= 0.177289271
Epoch: 0008 cost= 0.164127609
Epoch: 0009 cost= 0.157833308
Epoch: 0010 cost= 0.144638336
Epoch: 0011 cost= 0.140677395
Epoch: 0012 cost= 0.133864456
Epoch: 0013 cost= 0.127730995
Epoch: 0014 cost= 0.118851471
Epoch: 0015 cost= 0.111467114
Optimization Finished!
Accuracy: 0.116371
Error after 0 iterations:0.116371
Epoch: 0001 cost= 0.108530270
Epoch: 0002 cost= 0.103610612
Epoch: 0003 cost= 0.097405253
Epoch: 0004 cost= 0.094024596
Epoch: 0005 cost= 0.093340334
Epoch: 0006 cost= 0.088003223
Epoch: 0007 cost= 0.085662736
Epoch: 0008 cost= 0.082675809
Epoch: 0009 cost= 0.078721796
Epoch: 0010 cost= 0.076755011
Epoch: 0011 cost= 0.074962015
Epoch: 0012 cost= 0.073527814
Epoch: 0013 cost= 0.071860832
Epoch: 0014 cost= 0.071171053
Epoch: 0015 cost= 0.069432078
Optimization Finished!
Accuracy: 0.0720659
Error after 0 iterations:0.0720659
Epoch: 0001 cost= 0.068111829
Epoch: 0002 cost= 0.065833879
Epoch: 0003 cost= 0.064144029
Epoch: 0004 cost= 0.063975209
Epoch: 0005 cost= 0.062338471
Epoch: 0006 cost= 0.062348612
Epoch: 0007 cost= 0.061061291
Epoch: 0008 cost= 0.060648439
Epoch: 0009 cost= 0.060553673
Epoch: 0010 cost= 0.060807389
Epoch: 0011 cost= 0.058186249
Epoch: 0012 cost= 0.056882928
Epoch: 0013 cost= 0.058762704
Epoch: 0014 cost= 0.055880670
Epoch: 0015 cost= 0.056201088
Optimization Finished!
Accuracy: 0.0586148
Error after 0 iterations:0.0586148
Epoch: 0001 cost= 0.055328757
Epoch: 0002 cost= 0.054269627
Epoch: 0003 cost= 0.053509530
Epoch: 0004 cost= 0.054387462
Epoch: 0005 cost= 0.053372941
Epoch: 0006 cost= 0.053191369
Epoch: 0007 cost= 0.052677624
Epoch: 0008 cost= 0.052284450
Epoch: 0009 cost= 0.052285809
Epoch: 0010 cost= 0.051524123
Epoch: 0011 cost= 0.051916703
Epoch: 0012 cost= 0.049732319
Epoch: 0013 cost= 0.050014976
Epoch: 0014 cost= 0.052170128
Epoch: 0015 cost= 0.051498075
Optimization Finished!
Accuracy: 0.0529583
Error after 0 iterations:0.0529583
Epoch: 0001 cost= 0.050587058
Epoch: 0002 cost= 0.050166617
Epoch: 0003 cost= 0.048989912
Epoch: 0004 cost= 0.049623383
Epoch: 0005 cost= 0.049638755
Epoch: 0006 cost= 0.049672737
Epoch: 0007 cost= 0.048246326
Epoch: 0008 cost= 0.048316506
Epoch: 0009 cost= 0.048389547
Epoch: 0010 cost= 0.048693530
Epoch: 0011 cost= 0.048748256
Epoch: 0012 cost= 0.047338461
Epoch: 0013 cost= 0.048498962
Epoch: 0014 cost= 0.047443738
Epoch: 0015 cost= 0.046762998
Optimization Finished!
Accuracy: 0.0492356
Error after 0 iterations:0.0492356
Epoch: 0001 cost= 0.047116067
Epoch: 0002 cost= 0.047172682
Epoch: 0003 cost= 0.046057059
Epoch: 0004 cost= 0.046111906
Epoch: 0005 cost= 0.045710032
Epoch: 0006 cost= 0.046000168
Epoch: 0007 cost= 0.045531407
Epoch: 0008 cost= 0.045432775
Epoch: 0009 cost= 0.045454446
Epoch: 0010 cost= 0.043961347
Epoch: 0011 cost= 0.045129541
Epoch: 0012 cost= 0.045020971
Epoch: 0013 cost= 0.044415821
Epoch: 0014 cost= 0.044140649
Epoch: 0015 cost= 0.044298166
Optimization Finished!
Accuracy: 0.0461721
Error after 0 iterations:0.0461721
Epoch: 0001 cost= 0.044352701
Epoch: 0002 cost= 0.044014304
Epoch: 0003 cost= 0.043313056
Epoch: 0004 cost= 0.042851434
Epoch: 0005 cost= 0.043504942
Epoch: 0006 cost= 0.043543687
Epoch: 0007 cost= 0.043619787
Epoch: 0008 cost= 0.042892807
Epoch: 0009 cost= 0.043030921
Epoch: 0010 cost= 0.042539345
Epoch: 0011 cost= 0.042659108
Epoch: 0012 cost= 0.041961554
Epoch: 0013 cost= 0.042019218
Epoch: 0014 cost= 0.041404714
Epoch: 0015 cost= 0.042039464
Optimization Finished!
Accuracy: 0.0434011
Error after 0 iterations:0.0434011
Epoch: 0001 cost= 0.041004475
Epoch: 0002 cost= 0.041322179
Epoch: 0003 cost= 0.041880667
Epoch: 0004 cost= 0.041927729
Epoch: 0005 cost= 0.041055504
Epoch: 0006 cost= 0.041168907
Epoch: 0007 cost= 0.040588091
Epoch: 0008 cost= 0.040658668
Epoch: 0009 cost= 0.040443034
Epoch: 0010 cost= 0.039904846
Epoch: 0011 cost= 0.039756011
Epoch: 0012 cost= 0.039389238
Epoch: 0013 cost= 0.039364843
Epoch: 0014 cost= 0.039190865
Epoch: 0015 cost= 0.038655581
Optimization Finished!
Accuracy: 0.040828
Error after 0 iterations:0.040828
Epoch: 0001 cost= 0.039202049
Epoch: 0002 cost= 0.038811350
Epoch: 0003 cost= 0.039095000
Epoch: 0004 cost= 0.038905483
Epoch: 0005 cost= 0.038412438
Epoch: 0006 cost= 0.038811413
Epoch: 0007 cost= 0.038372810
Epoch: 0008 cost= 0.037580005
Epoch: 0009 cost= 0.037547339
Epoch: 0010 cost= 0.036990172
Epoch: 0011 cost= 0.037374827
Epoch: 0012 cost= 0.037655064
Epoch: 0013 cost= 0.037214465
Epoch: 0014 cost= 0.036333268
Epoch: 0015 cost= 0.036954226
Optimization Finished!
Accuracy: 0.0384154
Error after 0 iterations:0.0384154
Epoch: 0001 cost= 0.037494742
Epoch: 0002 cost= 0.036842323
Epoch: 0003 cost= 0.036692329
Epoch: 0004 cost= 0.036736737
Epoch: 0005 cost= 0.036211418
Epoch: 0006 cost= 0.036561669
Epoch: 0007 cost= 0.036009618
Epoch: 0008 cost= 0.035273619
Epoch: 0009 cost= 0.035483591
Epoch: 0010 cost= 0.035439119
Epoch: 0011 cost= 0.034741855
Epoch: 0012 cost= 0.034994063
Epoch: 0013 cost= 0.034491426
Epoch: 0014 cost= 0.035888284
Epoch: 0015 cost= 0.035026334
Optimization Finished!
Accuracy: 0.0361393
Error after 0 iterations:0.0361393
Epoch: 0001 cost= 0.034863828
Epoch: 0002 cost= 0.033851996
Epoch: 0003 cost= 0.034209137
Epoch: 0004 cost= 0.034110048
Epoch: 0005 cost= 0.033542195
Epoch: 0006 cost= 0.033974506
Epoch: 0007 cost= 0.033605482
Epoch: 0008 cost= 0.033168719
Epoch: 0009 cost= 0.034016981
Epoch: 0010 cost= 0.033600362
Epoch: 0011 cost= 0.033472549
Epoch: 0012 cost= 0.033220584
Epoch: 0013 cost= 0.032923812
Epoch: 0014 cost= 0.032749888
Epoch: 0015 cost= 0.032900994
Optimization Finished!
Accuracy: 0.0340098
Error after 0 iterations:0.0340098
Epoch: 0001 cost= 0.032377514
Epoch: 0002 cost= 0.032921007
Epoch: 0003 cost= 0.031378242
Epoch: 0004 cost= 0.031695091
Epoch: 0005 cost= 0.031689690
Epoch: 0006 cost= 0.031726261
Epoch: 0007 cost= 0.031619641
Epoch: 0008 cost= 0.031474600
Epoch: 0009 cost= 0.031396276
Epoch: 0010 cost= 0.031448537
Epoch: 0011 cost= 0.030709147
Epoch: 0012 cost= 0.030458616
Epoch: 0013 cost= 0.030813820
Epoch: 0014 cost= 0.031159821
Epoch: 0015 cost= 0.030910163
Optimization Finished!
Accuracy: 0.0320158
Error after 0 iterations:0.0320158
Epoch: 0001 cost= 0.030584219
Epoch: 0002 cost= 0.030294308
Epoch: 0003 cost= 0.029682229
Optimization Finished!
Accuracy: 0.0316336
Error after 0 iterations:0.0316336
Final params:
Epoch: 0001 cost= 2.153250885
Epoch: 0002 cost= 1.804174638
Epoch: 0003 cost= 1.539111209
Epoch: 0004 cost= 1.455615973
Epoch: 0005 cost= 1.349163866
Epoch: 0006 cost= 1.331781244
Epoch: 0007 cost= 1.280768108
Epoch: 0008 cost= 1.246967316
Epoch: 0009 cost= 1.197469497
Epoch: 0010 cost= 1.153349710
Epoch: 0011 cost= 1.137086391
Epoch: 0012 cost= 1.109253335
Epoch: 0013 cost= 1.083902597
Epoch: 0014 cost= 1.036632013
Epoch: 0015 cost= 1.032435083
Optimization Finished!
Accuracy: 1.0124
Error after 0 iterations:1.0124
Epoch: 0001 cost= 1.003325987
Epoch: 0002 cost= 0.958921885
Epoch: 0003 cost= 0.947094023
Epoch: 0004 cost= 0.905675697
Epoch: 0005 cost= 0.884653962
Epoch: 0006 cost= 0.855681050
Epoch: 0007 cost= 0.838958263
Epoch: 0008 cost= 0.821513450
Epoch: 0009 cost= 0.796317720
Epoch: 0010 cost= 0.763215446
Epoch: 0011 cost= 0.762239134
Epoch: 0012 cost= 0.729659200
Epoch: 0013 cost= 0.719209826
Epoch: 0014 cost= 0.706056643
Epoch: 0015 cost= 0.686763239
Optimization Finished!
Accuracy: 0.67369
Error after 0 iterations:0.67369
Epoch: 0001 cost= 0.661081719
Epoch: 0002 cost= 0.638005352
Epoch: 0003 cost= 0.629542208
Epoch: 0004 cost= 0.613008332
Epoch: 0005 cost= 0.598001862
Epoch: 0006 cost= 0.589007401
Epoch: 0007 cost= 0.561945367
Epoch: 0008 cost= 0.549934983
Epoch: 0009 cost= 0.534160697
Epoch: 0010 cost= 0.520826197
Epoch: 0011 cost= 0.513096201
Epoch: 0012 cost= 0.503378350
Epoch: 0013 cost= 0.489987469
Epoch: 0014 cost= 0.482842940
Epoch: 0015 cost= 0.471626127
Optimization Finished!
Accuracy: 0.466052
Error after 0 iterations:0.466052
Epoch: 0001 cost= 0.454421574
Epoch: 0002 cost= 0.451425004
Epoch: 0003 cost= 0.444326466
Epoch: 0004 cost= 0.422874475
Epoch: 0005 cost= 0.416678250
Epoch: 0006 cost= 0.404897356
Epoch: 0007 cost= 0.398117685
Epoch: 0008 cost= 0.389852732
Epoch: 0009 cost= 0.385298330
Epoch: 0010 cost= 0.380450100
Epoch: 0011 cost= 0.372862059
Epoch: 0012 cost= 0.358944321
Epoch: 0013 cost= 0.358271712
Epoch: 0014 cost= 0.346893454
Epoch: 0015 cost= 0.338346314
Optimization Finished!
Accuracy: 0.341791
Error after 0 iterations:0.341791
Epoch: 0001 cost= 0.338802487
Epoch: 0002 cost= 0.327329963
Epoch: 0003 cost= 0.326222551
Epoch: 0004 cost= 0.317774421
Epoch: 0005 cost= 0.315134096
Epoch: 0006 cost= 0.309625524
Epoch: 0007 cost= 0.297722125
Epoch: 0008 cost= 0.294333029
Epoch: 0009 cost= 0.291372329
Epoch: 0010 cost= 0.280576414
Epoch: 0011 cost= 0.282590783
Epoch: 0012 cost= 0.280875814
Epoch: 0013 cost= 0.275725561
Epoch: 0014 cost= 0.266715169
Epoch: 0015 cost= 0.263783389
Optimization Finished!
Accuracy: 0.264728
Error after 0 iterations:0.264728
Epoch: 0001 cost= 0.259091711
Epoch: 0002 cost= 0.262297302
Epoch: 0003 cost= 0.250337121
Epoch: 0004 cost= 0.248481354
Epoch: 0005 cost= 0.246383330
Epoch: 0006 cost= 0.236806405
Epoch: 0007 cost= 0.237767890
Epoch: 0008 cost= 0.234377974
Epoch: 0009 cost= 0.234383202
Epoch: 0010 cost= 0.227768120
Epoch: 0011 cost= 0.224253121
Epoch: 0012 cost= 0.226250657
Epoch: 0013 cost= 0.220459929
Epoch: 0014 cost= 0.214459601
Epoch: 0015 cost= 0.215837368
Optimization Finished!
Accuracy: 0.214427
Error after 0 iterations:0.214427
Epoch: 0001 cost= 0.209833369
Epoch: 0002 cost= 0.210850033
Epoch: 0003 cost= 0.202571708
Epoch: 0004 cost= 0.200894102
Epoch: 0005 cost= 0.202147439
Epoch: 0006 cost= 0.199411368
Epoch: 0007 cost= 0.194504476
Epoch: 0008 cost= 0.195573837
Epoch: 0009 cost= 0.189203465
Epoch: 0010 cost= 0.189494348
Epoch: 0011 cost= 0.187952915
Epoch: 0012 cost= 0.186398405
Epoch: 0013 cost= 0.183125234
Epoch: 0014 cost= 0.180573362
Epoch: 0015 cost= 0.178051075
Optimization Finished!
Accuracy: 0.179141
Error after 0 iterations:0.179141
Epoch: 0001 cost= 0.177260846
Epoch: 0002 cost= 0.175429049
Epoch: 0003 cost= 0.171969163
Epoch: 0004 cost= 0.173549345
Epoch: 0005 cost= 0.168656945
Epoch: 0006 cost= 0.169868648
Epoch: 0007 cost= 0.165499943
Epoch: 0008 cost= 0.163690984
Epoch: 0009 cost= 0.162210178
Epoch: 0010 cost= 0.159536850
Epoch: 0011 cost= 0.159727573
Epoch: 0012 cost= 0.158882630
Epoch: 0013 cost= 0.154032478
Epoch: 0014 cost= 0.152304581
Epoch: 0015 cost= 0.153382891
Optimization Finished!
Accuracy: 0.152719
Error after 0 iterations:0.152719
Epoch: 0001 cost= 0.151356986
Epoch: 0002 cost= 0.149712032
Epoch: 0003 cost= 0.147153836
Epoch: 0004 cost= 0.145788825
Epoch: 0005 cost= 0.144554868
Epoch: 0006 cost= 0.142735955
Epoch: 0007 cost= 0.140309894
Epoch: 0008 cost= 0.139752594
Epoch: 0009 cost= 0.138924503
Epoch: 0010 cost= 0.137713605
Epoch: 0011 cost= 0.134516403
Epoch: 0012 cost= 0.135307196
Epoch: 0013 cost= 0.133549860
Epoch: 0014 cost= 0.132527858
Epoch: 0015 cost= 0.129798117
Optimization Finished!
Accuracy: 0.131758
Error after 0 iterations:0.131758
Epoch: 0001 cost= 0.130185041
Epoch: 0002 cost= 0.127352861
Epoch: 0003 cost= 0.128001019
Epoch: 0004 cost= 0.126129331
Epoch: 0005 cost= 0.127352601
Epoch: 0006 cost= 0.124351455
Epoch: 0007 cost= 0.122488108
Epoch: 0008 cost= 0.123026235
Epoch: 0009 cost= 0.120317051
Epoch: 0010 cost= 0.120288672
Epoch: 0011 cost= 0.118210725
Epoch: 0012 cost= 0.117304151
Epoch: 0013 cost= 0.118306108
Epoch: 0014 cost= 0.116077329
Epoch: 0015 cost= 0.113189632
Optimization Finished!
Accuracy: 0.114459
Error after 0 iterations:0.114459
Epoch: 0001 cost= 0.112644327
Epoch: 0002 cost= 0.111335637
Epoch: 0003 cost= 0.110011293
Epoch: 0004 cost= 0.110019462
Epoch: 0005 cost= 0.109837207
Epoch: 0006 cost= 0.107298531
Epoch: 0007 cost= 0.106166454
Epoch: 0008 cost= 0.104861481
Epoch: 0009 cost= 0.106035918
Epoch: 0010 cost= 0.103736031
Epoch: 0011 cost= 0.104341105
Epoch: 0012 cost= 0.102008861
Epoch: 0013 cost= 0.100007924
Epoch: 0014 cost= 0.098622960
Epoch: 0015 cost= 0.100509036
Optimization Finished!
Accuracy: 0.100047
Error after 0 iterations:0.100047
Epoch: 0001 cost= 0.100218531
Epoch: 0002 cost= 0.098027411
Epoch: 0003 cost= 0.096838683
Epoch: 0004 cost= 0.095791672
Epoch: 0005 cost= 0.094068193
Epoch: 0006 cost= 0.093726520
Epoch: 0007 cost= 0.093080890
Epoch: 0008 cost= 0.092311913
Epoch: 0009 cost= 0.090730904
Epoch: 0010 cost= 0.089964806
Epoch: 0011 cost= 0.089241290
Epoch: 0012 cost= 0.089751078
Epoch: 0013 cost= 0.089253578
Epoch: 0014 cost= 0.086382055
Epoch: 0015 cost= 0.087097770
Optimization Finished!
Accuracy: 0.0877949
Error after 0 iterations:0.0877949
Epoch: 0001 cost= 0.087461133
Epoch: 0002 cost= 0.085000692
Epoch: 0003 cost= 0.086684242
Epoch: 0004 cost= 0.084245904
Epoch: 0005 cost= 0.083083582
Epoch: 0006 cost= 0.083913802
Epoch: 0007 cost= 0.082974900
Epoch: 0008 cost= 0.081456026
Epoch: 0009 cost= 0.079525383
Epoch: 0010 cost= 0.080837147
Epoch: 0011 cost= 0.079396878
Epoch: 0012 cost= 0.079075217
Epoch: 0013 cost= 0.078432117
Epoch: 0014 cost= 0.077063277
Epoch: 0015 cost= 0.076468357
Optimization Finished!
Accuracy: 0.0771543
Error after 0 iterations:0.0771543
Epoch: 0001 cost= 0.076073460
Epoch: 0002 cost= 0.076137170
Epoch: 0003 cost= 0.075005180
Epoch: 0004 cost= 0.073025182
Epoch: 0005 cost= 0.073297949
Epoch: 0006 cost= 0.073021975
Epoch: 0007 cost= 0.071994358
Epoch: 0008 cost= 0.070372878
Epoch: 0009 cost= 0.072327453
Epoch: 0010 cost= 0.070391212
Epoch: 0011 cost= 0.069802603
Epoch: 0012 cost= 0.069333269
Epoch: 0013 cost= 0.069305551
Epoch: 0014 cost= 0.067522933
Epoch: 0015 cost= 0.068157957
Optimization Finished!
Accuracy: 0.0679825
Error after 0 iterations:0.0679825
Epoch: 0001 cost= 0.066755553
Epoch: 0002 cost= 0.065708259
Epoch: 0003 cost= 0.065737726
Epoch: 0004 cost= 0.065957114
Epoch: 0005 cost= 0.065175971
Epoch: 0006 cost= 0.063984771
Epoch: 0007 cost= 0.062442797
Epoch: 0008 cost= 0.063970540
Epoch: 0009 cost= 0.062907372
Epoch: 0010 cost= 0.061668003
Epoch: 0011 cost= 0.062060430
Epoch: 0012 cost= 0.060667806
Epoch: 0013 cost= 0.060583036
Epoch: 0014 cost= 0.059652326
Epoch: 0015 cost= 0.059676663
Optimization Finished!
Accuracy: 0.06
Error after 0 iterations:0.06
Epoch: 0001 cost= 0.059430031
Epoch: 0002 cost= 0.058556373
Epoch: 0003 cost= 0.058407460
Epoch: 0004 cost= 0.057874399
Epoch: 0005 cost= 0.057162952
Epoch: 0006 cost= 0.056184979
Epoch: 0007 cost= 0.055471664
Epoch: 0008 cost= 0.055760705
Epoch: 0009 cost= 0.055183241
Epoch: 0010 cost= 0.054489894
Epoch: 0011 cost= 0.054006399
Epoch: 0012 cost= 0.055359671
Epoch: 0013 cost= 0.053854081
Epoch: 0014 cost= 0.053625448
Epoch: 0015 cost= 0.052769984
Optimization Finished!
Accuracy: 0.0530092
Error after 0 iterations:0.0530092
Epoch: 0001 cost= 0.052652207
Epoch: 0002 cost= 0.051520838
Epoch: 0003 cost= 0.051383858
Epoch: 0004 cost= 0.051033665
Epoch: 0005 cost= 0.050339090
Epoch: 0006 cost= 0.050472313
Epoch: 0007 cost= 0.049806403
Epoch: 0008 cost= 0.048389986
Epoch: 0009 cost= 0.048684753
Epoch: 0010 cost= 0.048653018
Epoch: 0011 cost= 0.047735391
Epoch: 0012 cost= 0.047441486
Epoch: 0013 cost= 0.048214061
Epoch: 0014 cost= 0.047692351
Epoch: 0015 cost= 0.046776028
Optimization Finished!
Accuracy: 0.0468665
Error after 0 iterations:0.0468665
Epoch: 0001 cost= 0.046605101
Epoch: 0002 cost= 0.046579813
Epoch: 0003 cost= 0.046035251
Epoch: 0004 cost= 0.044648232
Epoch: 0005 cost= 0.044250334
Epoch: 0006 cost= 0.043821619
Epoch: 0007 cost= 0.044350036
Epoch: 0008 cost= 0.043211949
Epoch: 0009 cost= 0.042740952
Epoch: 0010 cost= 0.043870565
Epoch: 0011 cost= 0.042785692
Epoch: 0012 cost= 0.042190788
Epoch: 0013 cost= 0.041707557
Epoch: 0014 cost= 0.041354738
Epoch: 0015 cost= 0.041569514
Optimization Finished!
Accuracy: 0.0414859
Error after 0 iterations:0.0414859
Epoch: 0001 cost= 0.040888625
Epoch: 0002 cost= 0.040152247
Epoch: 0003 cost= 0.040168446
Epoch: 0004 cost= 0.039678898
Epoch: 0005 cost= 0.039992565
Epoch: 0006 cost= 0.039236027
Epoch: 0007 cost= 0.039269242
Epoch: 0008 cost= 0.038402133
Epoch: 0009 cost= 0.038778602
Epoch: 0010 cost= 0.037979589
Epoch: 0011 cost= 0.038059596
Epoch: 0012 cost= 0.037871503
Epoch: 0013 cost= 0.036991493
Epoch: 0014 cost= 0.036568258
Epoch: 0015 cost= 0.036499316
Optimization Finished!
Accuracy: 0.0367412
Error after 0 iterations:0.0367412
Epoch: 0001 cost= 0.036373124
Epoch: 0002 cost= 0.035998760
Epoch: 0003 cost= 0.035846806
Epoch: 0004 cost= 0.035147348
Epoch: 0005 cost= 0.035115007
Epoch: 0006 cost= 0.034796211
Epoch: 0007 cost= 0.034257598
Epoch: 0008 cost= 0.034307435
Epoch: 0009 cost= 0.033498313
Epoch: 0010 cost= 0.033446287
Epoch: 0011 cost= 0.033885764
Epoch: 0012 cost= 0.033795217
Epoch: 0013 cost= 0.033126388
Epoch: 0014 cost= 0.031955666
Epoch: 0015 cost= 0.032805569
Optimization Finished!
Accuracy: 0.0325858
Error after 0 iterations:0.0325858
Epoch: 0001 cost= 0.031607189
Epoch: 0002 cost= 0.032143378
Epoch: 0003 cost= 0.031577119
Epoch: 0004 cost= 0.031221864
Epoch: 0005 cost= 0.031262278
Epoch: 0006 cost= 0.031359574
Epoch: 0007 cost= 0.030523705
Epoch: 0008 cost= 0.030399058
Epoch: 0009 cost= 0.030055944
Epoch: 0010 cost= 0.029896392
Optimization Finished!
Accuracy: 0.0300677
Error after 0 iterations:0.0300677
Final params:
Epoch: 0001 cost= 5.741192436
Epoch: 0002 cost= 4.328470278
Epoch: 0003 cost= 3.243019962
Epoch: 0004 cost= 2.490483427
Epoch: 0005 cost= 1.940615439
Epoch: 0006 cost= 1.519821858
Epoch: 0007 cost= 1.204393744
Epoch: 0008 cost= 0.959296274
Epoch: 0009 cost= 0.783492732
Epoch: 0010 cost= 0.635659838
Epoch: 0011 cost= 0.536593646
Epoch: 0012 cost= 0.466122472
Epoch: 0013 cost= 0.403418165
Epoch: 0014 cost= 0.356449246
Epoch: 0015 cost= 0.327002120
Optimization Finished!
Accuracy: 0.309406
Error after 0 iterations:0.309406
Epoch: 0001 cost= 0.304701823
Epoch: 0002 cost= 0.277886474
Epoch: 0003 cost= 0.269563997
Epoch: 0004 cost= 0.253170967
Epoch: 0005 cost= 0.247052932
Epoch: 0006 cost= 0.242273822
Epoch: 0007 cost= 0.229419532
Epoch: 0008 cost= 0.225323951
Epoch: 0009 cost= 0.226979306
Epoch: 0010 cost= 0.222576562
Epoch: 0011 cost= 0.216240916
Epoch: 0012 cost= 0.215680283
Epoch: 0013 cost= 0.221135810
Epoch: 0014 cost= 0.213215250
Epoch: 0015 cost= 0.208143440
Optimization Finished!
Accuracy: 0.208432
Error after 0 iterations:0.208432
Epoch: 0001 cost= 0.206150940
Epoch: 0002 cost= 0.208347410
Epoch: 0003 cost= 0.206683290
Epoch: 0004 cost= 0.205578244
Epoch: 0005 cost= 0.202891061
Epoch: 0006 cost= 0.200661618
Epoch: 0007 cost= 0.204632944
Epoch: 0008 cost= 0.203505707
Epoch: 0009 cost= 0.201500592
Epoch: 0010 cost= 0.199385044
Epoch: 0011 cost= 0.199251682
Epoch: 0012 cost= 0.200139517
Epoch: 0013 cost= 0.195589480
Epoch: 0014 cost= 0.193230838
Epoch: 0015 cost= 0.194838408
Optimization Finished!
Accuracy: 0.193655
Error after 0 iterations:0.193655
Epoch: 0001 cost= 0.197029352
Epoch: 0002 cost= 0.191639164
Epoch: 0003 cost= 0.191846141
Epoch: 0004 cost= 0.196893501
Epoch: 0005 cost= 0.193071866
Epoch: 0006 cost= 0.192794749
Epoch: 0007 cost= 0.185641333
Epoch: 0008 cost= 0.188947198
Epoch: 0009 cost= 0.187752390
Epoch: 0010 cost= 0.187636277
Epoch: 0011 cost= 0.186060688
Epoch: 0012 cost= 0.187897381
Epoch: 0013 cost= 0.183320475
Epoch: 0014 cost= 0.183521244
Epoch: 0015 cost= 0.185227045
Optimization Finished!
Accuracy: 0.179585
Error after 0 iterations:0.179585
Epoch: 0001 cost= 0.183741975
Epoch: 0002 cost= 0.183505562
Epoch: 0003 cost= 0.180550224
Epoch: 0004 cost= 0.181009585
Epoch: 0005 cost= 0.177896541
Epoch: 0006 cost= 0.177608660
Epoch: 0007 cost= 0.176532167
Epoch: 0008 cost= 0.173318377
Epoch: 0009 cost= 0.170522118
Epoch: 0010 cost= 0.174733174
Epoch: 0011 cost= 0.170988521
Epoch: 0012 cost= 0.168776470
Epoch: 0013 cost= 0.168679410
Epoch: 0014 cost= 0.167922494
Epoch: 0015 cost= 0.165855485
Optimization Finished!
Accuracy: 0.165268
Error after 0 iterations:0.165268
Epoch: 0001 cost= 0.166007924
Epoch: 0002 cost= 0.165720144
Epoch: 0003 cost= 0.168363506
Epoch: 0004 cost= 0.164487886
Epoch: 0005 cost= 0.160917747
Epoch: 0006 cost= 0.163544056
Epoch: 0007 cost= 0.163262329
Epoch: 0008 cost= 0.162720448
Epoch: 0009 cost= 0.159543234
Epoch: 0010 cost= 0.162844545
Epoch: 0011 cost= 0.156956497
Epoch: 0012 cost= 0.152479869
Epoch: 0013 cost= 0.153822240
Epoch: 0014 cost= 0.155445635
Epoch: 0015 cost= 0.154939011
Optimization Finished!
Accuracy: 0.151101
Error after 0 iterations:0.151101
Epoch: 0001 cost= 0.150459054
Epoch: 0002 cost= 0.147897226
Epoch: 0003 cost= 0.149981683
Epoch: 0004 cost= 0.150396824
Epoch: 0005 cost= 0.150887850
Epoch: 0006 cost= 0.147247946
Epoch: 0007 cost= 0.147623855
Epoch: 0008 cost= 0.146101829
Epoch: 0009 cost= 0.143512848
Epoch: 0010 cost= 0.142704448
Epoch: 0011 cost= 0.145608589
Epoch: 0012 cost= 0.141773400
Epoch: 0013 cost= 0.141302976
Epoch: 0014 cost= 0.142476177
Epoch: 0015 cost= 0.142214906
Optimization Finished!
Accuracy: 0.138078
Error after 0 iterations:0.138078
Epoch: 0001 cost= 0.140412700
Epoch: 0002 cost= 0.139704615
Epoch: 0003 cost= 0.138622338
Epoch: 0004 cost= 0.137251425
Epoch: 0005 cost= 0.136140579
Epoch: 0006 cost= 0.132955071
Epoch: 0007 cost= 0.135899982
Epoch: 0008 cost= 0.134810817
Epoch: 0009 cost= 0.134689048
Epoch: 0010 cost= 0.132358557
Epoch: 0011 cost= 0.131928360
Epoch: 0012 cost= 0.132347536
Epoch: 0013 cost= 0.131165257
Epoch: 0014 cost= 0.132737911
Epoch: 0015 cost= 0.128977618
Optimization Finished!
Accuracy: 0.127068
Error after 0 iterations:0.127068
Epoch: 0001 cost= 0.127690062
Epoch: 0002 cost= 0.129966465
Epoch: 0003 cost= 0.128901069
Epoch: 0004 cost= 0.123621042
Epoch: 0005 cost= 0.127214071
Epoch: 0006 cost= 0.124614839
Epoch: 0007 cost= 0.123723647
Epoch: 0008 cost= 0.123416276
Epoch: 0009 cost= 0.123193866
Epoch: 0010 cost= 0.124040067
Epoch: 0011 cost= 0.123748624
Epoch: 0012 cost= 0.122277167
Epoch: 0013 cost= 0.120833364
Epoch: 0014 cost= 0.119449045
Epoch: 0015 cost= 0.120110938
Optimization Finished!
Accuracy: 0.118485
Error after 0 iterations:0.118485
Epoch: 0001 cost= 0.121541739
Epoch: 0002 cost= 0.118318926
Epoch: 0003 cost= 0.117869441
Epoch: 0004 cost= 0.118248917
Epoch: 0005 cost= 0.119651335
Epoch: 0006 cost= 0.117062913
Epoch: 0007 cost= 0.119337195
Epoch: 0008 cost= 0.116563667
Epoch: 0009 cost= 0.116447046
Epoch: 0010 cost= 0.115081581
Epoch: 0011 cost= 0.116177598
Epoch: 0012 cost= 0.114624722
Epoch: 0013 cost= 0.114598507
Epoch: 0014 cost= 0.114869893
Epoch: 0015 cost= 0.112349288
Optimization Finished!
Accuracy: 0.111549
Error after 0 iterations:0.111549
Epoch: 0001 cost= 0.112975976
Epoch: 0002 cost= 0.111977141
Epoch: 0003 cost= 0.109693715
Epoch: 0004 cost= 0.109649724
Epoch: 0005 cost= 0.113484018
Epoch: 0006 cost= 0.111505154
Epoch: 0007 cost= 0.109586978
Epoch: 0008 cost= 0.110760105
Epoch: 0009 cost= 0.111237790
Epoch: 0010 cost= 0.109943667
Epoch: 0011 cost= 0.109310788
Epoch: 0012 cost= 0.107451957
Epoch: 0013 cost= 0.106609660
Epoch: 0014 cost= 0.107819472
Epoch: 0015 cost= 0.105293433
Optimization Finished!
Accuracy: 0.105454
Error after 0 iterations:0.105454
Epoch: 0001 cost= 0.109315199
Epoch: 0002 cost= 0.106582122
Epoch: 0003 cost= 0.105373015
Epoch: 0004 cost= 0.106213322
Epoch: 0005 cost= 0.104188484
Epoch: 0006 cost= 0.106205036
Epoch: 0007 cost= 0.105159022
Epoch: 0008 cost= 0.104731341
Epoch: 0009 cost= 0.103390452
Epoch: 0010 cost= 0.103943533
Epoch: 0011 cost= 0.102719550
Epoch: 0012 cost= 0.102320577
Epoch: 0013 cost= 0.103059262
Epoch: 0014 cost= 0.102030639
Epoch: 0015 cost= 0.100320600
Optimization Finished!
Accuracy: 0.0998356
Error after 0 iterations:0.0998356
Epoch: 0001 cost= 0.103013316
Epoch: 0002 cost= 0.100530747
Epoch: 0003 cost= 0.099891211
Epoch: 0004 cost= 0.101260872
Epoch: 0005 cost= 0.099099778
Epoch: 0006 cost= 0.099478443
Epoch: 0007 cost= 0.100370869
Epoch: 0008 cost= 0.097378832
Epoch: 0009 cost= 0.098672053
Epoch: 0010 cost= 0.099324477
Epoch: 0011 cost= 0.098703726
Epoch: 0012 cost= 0.097915721
Epoch: 0013 cost= 0.097142673
Epoch: 0014 cost= 0.098467641
Epoch: 0015 cost= 0.094480708
Optimization Finished!
Accuracy: 0.0946278
Error after 0 iterations:0.0946278
Epoch: 0001 cost= 0.096483962
Epoch: 0002 cost= 0.096440910
Epoch: 0003 cost= 0.096042049
Epoch: 0004 cost= 0.094514029
Epoch: 0005 cost= 0.093393709
Epoch: 0006 cost= 0.095028700
Epoch: 0007 cost= 0.092035803
Epoch: 0008 cost= 0.095612793
Epoch: 0009 cost= 0.093491593
Epoch: 0010 cost= 0.093278359
Epoch: 0011 cost= 0.092811555
Epoch: 0012 cost= 0.092212933
Epoch: 0013 cost= 0.089791076
Epoch: 0014 cost= 0.091437611
Epoch: 0015 cost= 0.092694663
Optimization Finished!
Accuracy: 0.0898237
Error after 0 iterations:0.0898237
Epoch: 0001 cost= 0.092278320
Epoch: 0002 cost= 0.090927613
Epoch: 0003 cost= 0.090675627
Epoch: 0004 cost= 0.089924957
Epoch: 0005 cost= 0.088826801
Epoch: 0006 cost= 0.089680360
Epoch: 0007 cost= 0.089585143
Epoch: 0008 cost= 0.088774322
Epoch: 0009 cost= 0.089189430
Epoch: 0010 cost= 0.089606534
Epoch: 0011 cost= 0.087612534
Epoch: 0012 cost= 0.087739767
Epoch: 0013 cost= 0.085951330
Epoch: 0014 cost= 0.087666512
Epoch: 0015 cost= 0.085443063
Optimization Finished!
Accuracy: 0.0853933
Error after 0 iterations:0.0853933
Epoch: 0001 cost= 0.086287552
Epoch: 0002 cost= 0.085192411
Epoch: 0003 cost= 0.085677591
Epoch: 0004 cost= 0.086439715
Epoch: 0005 cost= 0.086851470
Epoch: 0006 cost= 0.085669252
Epoch: 0007 cost= 0.084180009
Epoch: 0008 cost= 0.085652433
Epoch: 0009 cost= 0.083844420
Epoch: 0010 cost= 0.082880823
Epoch: 0011 cost= 0.085252899
Epoch: 0012 cost= 0.083692376
Epoch: 0013 cost= 0.083482680
Epoch: 0014 cost= 0.081775460
Epoch: 0015 cost= 0.083807443
Optimization Finished!
Accuracy: 0.0812771
Error after 0 iterations:0.0812771
Epoch: 0001 cost= 0.080964437
Epoch: 0002 cost= 0.080754551
Epoch: 0003 cost= 0.082616818
Epoch: 0004 cost= 0.081654212
Epoch: 0005 cost= 0.081276731
Epoch: 0006 cost= 0.080849329
Epoch: 0007 cost= 0.081038435
Epoch: 0008 cost= 0.081671730
Epoch: 0009 cost= 0.081521635
Epoch: 0010 cost= 0.081703418
Epoch: 0011 cost= 0.081090088
Epoch: 0012 cost= 0.079997997
Epoch: 0013 cost= 0.079266238
Epoch: 0014 cost= 0.080299020
Epoch: 0015 cost= 0.080089171
Optimization Finished!
Accuracy: 0.0774622
Error after 0 iterations:0.0774622
Epoch: 0001 cost= 0.077604887
Epoch: 0002 cost= 0.077828173
Epoch: 0003 cost= 0.077371141
Epoch: 0004 cost= 0.077488659
Epoch: 0005 cost= 0.078475912
Epoch: 0006 cost= 0.076746677
Epoch: 0007 cost= 0.078260107
Epoch: 0008 cost= 0.076898797
Epoch: 0009 cost= 0.076764241
Epoch: 0010 cost= 0.076349391
Epoch: 0011 cost= 0.076109102
Epoch: 0012 cost= 0.075284286
Epoch: 0013 cost= 0.076386683
Epoch: 0014 cost= 0.075295267
Epoch: 0015 cost= 0.074174370
Optimization Finished!
Accuracy: 0.0739459
Error after 0 iterations:0.0739459
Epoch: 0001 cost= 0.075772457
Epoch: 0002 cost= 0.074158104
Epoch: 0003 cost= 0.074765223
Epoch: 0004 cost= 0.073761031
Epoch: 0005 cost= 0.073083837
Epoch: 0006 cost= 0.073354490
Epoch: 0007 cost= 0.074061257
Epoch: 0008 cost= 0.073813777
Epoch: 0009 cost= 0.073842695
Epoch: 0010 cost= 0.073883100
Epoch: 0011 cost= 0.072857708
Epoch: 0012 cost= 0.071718910
Epoch: 0013 cost= 0.071753292
Epoch: 0014 cost= 0.071738340
Epoch: 0015 cost= 0.070881978
Optimization Finished!
Accuracy: 0.0706663
Error after 0 iterations:0.0706663
Epoch: 0001 cost= 0.071174632
Epoch: 0002 cost= 0.071420929
Epoch: 0003 cost= 0.070769350
Epoch: 0004 cost= 0.070482801
Epoch: 0005 cost= 0.070650136
Epoch: 0006 cost= 0.070454006
Epoch: 0007 cost= 0.070591168
Epoch: 0008 cost= 0.071785472
Epoch: 0009 cost= 0.069226873
Epoch: 0010 cost= 0.070277350
Epoch: 0011 cost= 0.069968003
Epoch: 0012 cost= 0.069619252
Epoch: 0013 cost= 0.068915808
Epoch: 0014 cost= 0.068655133
Epoch: 0015 cost= 0.068309440
Optimization Finished!
Accuracy: 0.0675809
Error after 0 iterations:0.0675809
Epoch: 0001 cost= 0.068498312
Epoch: 0002 cost= 0.067190093
Epoch: 0003 cost= 0.067496227
Epoch: 0004 cost= 0.067331171
Epoch: 0005 cost= 0.067048326
Epoch: 0006 cost= 0.067932622
Epoch: 0007 cost= 0.067398570
Epoch: 0008 cost= 0.067289029
Epoch: 0009 cost= 0.067003274
Epoch: 0010 cost= 0.068066585
Epoch: 0011 cost= 0.067311986
Epoch: 0012 cost= 0.066576275
Epoch: 0013 cost= 0.065896198
Epoch: 0014 cost= 0.066756119
Epoch: 0015 cost= 0.064661129
Optimization Finished!
Accuracy: 0.0646661
Error after 0 iterations:0.0646661
Epoch: 0001 cost= 0.066083761
Epoch: 0002 cost= 0.065779109
Epoch: 0003 cost= 0.064683409
Epoch: 0004 cost= 0.065385781
Epoch: 0005 cost= 0.065024011
Epoch: 0006 cost= 0.064185743
Epoch: 0007 cost= 0.064797069
Epoch: 0008 cost= 0.064738126
Epoch: 0009 cost= 0.064194605
Epoch: 0010 cost= 0.064209320
Epoch: 0011 cost= 0.064311020
Epoch: 0012 cost= 0.064099460
Epoch: 0013 cost= 0.063276039
Epoch: 0014 cost= 0.062171466
Epoch: 0015 cost= 0.063168596
Optimization Finished!
Accuracy: 0.0618866
Error after 0 iterations:0.0618866
Epoch: 0001 cost= 0.063154845
Epoch: 0002 cost= 0.063565573
Epoch: 0003 cost= 0.062337355
Epoch: 0004 cost= 0.062479474
Epoch: 0005 cost= 0.061683150
Epoch: 0006 cost= 0.062617555
Epoch: 0007 cost= 0.061125527
Epoch: 0008 cost= 0.061509126
Epoch: 0009 cost= 0.061696135
Epoch: 0010 cost= 0.061775123
Epoch: 0011 cost= 0.060953636
Epoch: 0012 cost= 0.060301587
Epoch: 0013 cost= 0.061315972
Epoch: 0014 cost= 0.061073454
Epoch: 0015 cost= 0.061036996
Optimization Finished!
Accuracy: 0.0592178
Error after 0 iterations:0.0592178
Epoch: 0001 cost= 0.059831577
Epoch: 0002 cost= 0.061104488
Epoch: 0003 cost= 0.060011961
Epoch: 0004 cost= 0.059060131
Epoch: 0005 cost= 0.059589367
Epoch: 0006 cost= 0.060380357
Epoch: 0007 cost= 0.059803703
Epoch: 0008 cost= 0.059705813
Epoch: 0009 cost= 0.059363095
Epoch: 0010 cost= 0.058945500
Epoch: 0011 cost= 0.059328524
Epoch: 0012 cost= 0.057513339
Epoch: 0013 cost= 0.057813858
Epoch: 0014 cost= 0.057679582
Epoch: 0015 cost= 0.058418155
Optimization Finished!
Accuracy: 0.056651
Error after 0 iterations:0.056651
Epoch: 0001 cost= 0.057420521
Epoch: 0002 cost= 0.056760085
Epoch: 0003 cost= 0.056689204
Epoch: 0004 cost= 0.057182281
Epoch: 0005 cost= 0.056796981
Epoch: 0006 cost= 0.056778309
Epoch: 0007 cost= 0.056232367
Epoch: 0008 cost= 0.056623285
Epoch: 0009 cost= 0.056241795
Epoch: 0010 cost= 0.056221304
Epoch: 0011 cost= 0.056123088
Epoch: 0012 cost= 0.055433755
Epoch: 0013 cost= 0.055372062
Epoch: 0014 cost= 0.055275566
Epoch: 0015 cost= 0.055104521
Optimization Finished!
Accuracy: 0.0542048
Error after 0 iterations:0.0542048
Epoch: 0001 cost= 0.055001517
Epoch: 0002 cost= 0.054406364
Epoch: 0003 cost= 0.054703572
Epoch: 0004 cost= 0.054682212
Epoch: 0005 cost= 0.054018528
Epoch: 0006 cost= 0.054293932
Epoch: 0007 cost= 0.054372363
Epoch: 0008 cost= 0.053278290
Epoch: 0009 cost= 0.054238551
Epoch: 0010 cost= 0.052932516
Epoch: 0011 cost= 0.053422098
Epoch: 0012 cost= 0.052872264
Epoch: 0013 cost= 0.054227617
Epoch: 0014 cost= 0.053556415
Epoch: 0015 cost= 0.053702009
Optimization Finished!
Accuracy: 0.0518444
Error after 0 iterations:0.0518444
Epoch: 0001 cost= 0.052794431
Epoch: 0002 cost= 0.052908505
Epoch: 0003 cost= 0.051839483
Epoch: 0004 cost= 0.052019229
Epoch: 0005 cost= 0.052927566
Epoch: 0006 cost= 0.052053015
Epoch: 0007 cost= 0.050636799
Epoch: 0008 cost= 0.052250235
Epoch: 0009 cost= 0.052194465
Epoch: 0010 cost= 0.051602188
Epoch: 0011 cost= 0.050730994
Epoch: 0012 cost= 0.050770231
Epoch: 0013 cost= 0.050898856
Epoch: 0014 cost= 0.050420872
Epoch: 0015 cost= 0.050450491
Optimization Finished!
Accuracy: 0.0495691
Error after 0 iterations:0.0495691
Epoch: 0001 cost= 0.050729001
Epoch: 0002 cost= 0.050779209
Epoch: 0003 cost= 0.049488357
Epoch: 0004 cost= 0.050094043
Epoch: 0005 cost= 0.050725534
Epoch: 0006 cost= 0.049315766
Epoch: 0007 cost= 0.048694599
Epoch: 0008 cost= 0.049277544
Epoch: 0009 cost= 0.049405044
Epoch: 0010 cost= 0.049055506
Epoch: 0011 cost= 0.049769895
Epoch: 0012 cost= 0.048682372
Epoch: 0013 cost= 0.048106942
Epoch: 0014 cost= 0.048044083
Epoch: 0015 cost= 0.048512507
Optimization Finished!
Accuracy: 0.0473741
Error after 0 iterations:0.0473741
Epoch: 0001 cost= 0.047919007
Epoch: 0002 cost= 0.048575283
Epoch: 0003 cost= 0.047893442
Epoch: 0004 cost= 0.047100134
Epoch: 0005 cost= 0.048453779
Epoch: 0006 cost= 0.047868079
Epoch: 0007 cost= 0.047218053
Epoch: 0008 cost= 0.046631162
Epoch: 0009 cost= 0.046238610
Epoch: 0010 cost= 0.046947575
Epoch: 0011 cost= 0.046840807
Epoch: 0012 cost= 0.046385854
Epoch: 0013 cost= 0.044814575
Epoch: 0014 cost= 0.046587998
Epoch: 0015 cost= 0.046400008
Optimization Finished!
Accuracy: 0.0452656
Error after 0 iterations:0.0452656
Epoch: 0001 cost= 0.045775688
Epoch: 0002 cost= 0.046974214
Epoch: 0003 cost= 0.045438068
Epoch: 0004 cost= 0.045184109
Epoch: 0005 cost= 0.045905033
Epoch: 0006 cost= 0.045146245
Epoch: 0007 cost= 0.045200012
Epoch: 0008 cost= 0.045251518
Epoch: 0009 cost= 0.044873844
Epoch: 0010 cost= 0.044383972
Epoch: 0011 cost= 0.044842315
Epoch: 0012 cost= 0.044971274
Epoch: 0013 cost= 0.043889083
Epoch: 0014 cost= 0.044201162
Epoch: 0015 cost= 0.044824280
Optimization Finished!
Accuracy: 0.0432208
Error after 0 iterations:0.0432208
Epoch: 0001 cost= 0.043698945
Epoch: 0002 cost= 0.043810130
Epoch: 0003 cost= 0.043719260
Epoch: 0004 cost= 0.042919728
Epoch: 0005 cost= 0.044250261
Epoch: 0006 cost= 0.043589570
Epoch: 0007 cost= 0.042902132
Epoch: 0008 cost= 0.043040589
Epoch: 0009 cost= 0.043039836
Epoch: 0010 cost= 0.042936151
Epoch: 0011 cost= 0.043441915
Epoch: 0012 cost= 0.042283104
Epoch: 0013 cost= 0.042313121
Epoch: 0014 cost= 0.041566924
Epoch: 0015 cost= 0.042679968
Optimization Finished!
Accuracy: 0.0412455
Error after 0 iterations:0.0412455
Epoch: 0001 cost= 0.041706633
Epoch: 0002 cost= 0.043156004
Epoch: 0003 cost= 0.041154356
Epoch: 0004 cost= 0.041278708
Epoch: 0005 cost= 0.041543091
Epoch: 0006 cost= 0.041486648
Epoch: 0007 cost= 0.042098775
Epoch: 0008 cost= 0.041211608
Epoch: 0009 cost= 0.041105841
Epoch: 0010 cost= 0.040890056
Epoch: 0011 cost= 0.041252983
Epoch: 0012 cost= 0.040980859
Epoch: 0013 cost= 0.040236490
Epoch: 0014 cost= 0.040952647
Epoch: 0015 cost= 0.040222070
Optimization Finished!
Accuracy: 0.0393389
Error after 0 iterations:0.0393389
Epoch: 0001 cost= 0.040479585
Epoch: 0002 cost= 0.039465898
Epoch: 0003 cost= 0.038978461
Epoch: 0004 cost= 0.040053094
Epoch: 0005 cost= 0.040352893
Epoch: 0006 cost= 0.039285488
Epoch: 0007 cost= 0.039304049
Epoch: 0008 cost= 0.039592886
Epoch: 0009 cost= 0.039255056
Epoch: 0010 cost= 0.037869886
Epoch: 0011 cost= 0.039484539
Epoch: 0012 cost= 0.038755950
Epoch: 0013 cost= 0.038366365
Epoch: 0014 cost= 0.037756750
Epoch: 0015 cost= 0.038489517
Optimization Finished!
Accuracy: 0.0375045
Error after 0 iterations:0.0375045
Epoch: 0001 cost= 0.038218919
Epoch: 0002 cost= 0.037965825
Epoch: 0003 cost= 0.038093860
Epoch: 0004 cost= 0.038115632
Epoch: 0005 cost= 0.038482909
Epoch: 0006 cost= 0.037626555
Epoch: 0007 cost= 0.037460326
Epoch: 0008 cost= 0.037382912
Epoch: 0009 cost= 0.037405121
Epoch: 0010 cost= 0.036703122
Epoch: 0011 cost= 0.037190352
Epoch: 0012 cost= 0.037456324
Epoch: 0013 cost= 0.036033516
Epoch: 0014 cost= 0.036371263
Epoch: 0015 cost= 0.036838007
Optimization Finished!
Accuracy: 0.0357335
Error after 0 iterations:0.0357335
Epoch: 0001 cost= 0.036248718
Epoch: 0002 cost= 0.036096963
Epoch: 0003 cost= 0.035062204
Epoch: 0004 cost= 0.036261660
Epoch: 0005 cost= 0.035783947
Epoch: 0006 cost= 0.035260338
Epoch: 0007 cost= 0.035456808
Epoch: 0008 cost= 0.035777455
Epoch: 0009 cost= 0.035532211
Epoch: 0010 cost= 0.035477246
Epoch: 0011 cost= 0.035029975
Epoch: 0012 cost= 0.035140380
Epoch: 0013 cost= 0.035238191
Epoch: 0014 cost= 0.034741589
Epoch: 0015 cost= 0.034717666
Optimization Finished!
Accuracy: 0.0340711
Error after 0 iterations:0.0340711
Epoch: 0001 cost= 0.035293444
Epoch: 0002 cost= 0.034097296
Epoch: 0003 cost= 0.034749920
Epoch: 0004 cost= 0.034715894
Epoch: 0005 cost= 0.034640325
Epoch: 0006 cost= 0.034573245
Epoch: 0007 cost= 0.034905485
Epoch: 0008 cost= 0.033962733
Epoch: 0009 cost= 0.034233553
Epoch: 0010 cost= 0.034388909
Epoch: 0011 cost= 0.033588481
Epoch: 0012 cost= 0.034044032
Epoch: 0013 cost= 0.033354589
Epoch: 0014 cost= 0.033949519
Epoch: 0015 cost= 0.033558922
Optimization Finished!
Accuracy: 0.0327269
Error after 0 iterations:0.0327269
Epoch: 0001 cost= 0.033897788
Epoch: 0002 cost= 0.033925320
Epoch: 0003 cost= 0.033644528
Epoch: 0004 cost= 0.033424485
Epoch: 0005 cost= 0.033167912
Epoch: 0006 cost= 0.033406701
Epoch: 0007 cost= 0.033191689
Epoch: 0008 cost= 0.033609696
Epoch: 0009 cost= 0.032403889
Epoch: 0010 cost= 0.033151957
Epoch: 0011 cost= 0.033091874
Epoch: 0012 cost= 0.033301407
Epoch: 0013 cost= 0.033126943
Epoch: 0014 cost= 0.032826825
Epoch: 0015 cost= 0.032481287
Optimization Finished!
Accuracy: 0.0316168
Error after 0 iterations:0.0316168
Epoch: 0001 cost= 0.032383410
Epoch: 0002 cost= 0.032674338
Epoch: 0003 cost= 0.032855576
Epoch: 0004 cost= 0.032542764
Epoch: 0005 cost= 0.032405250
Epoch: 0006 cost= 0.032017721
Epoch: 0007 cost= 0.032002686
Epoch: 0008 cost= 0.032286127
Epoch: 0009 cost= 0.031775218
Epoch: 0010 cost= 0.031069607
Epoch: 0011 cost= 0.031287309
Epoch: 0012 cost= 0.031725089
Epoch: 0013 cost= 0.031685343
Epoch: 0014 cost= 0.031730041
Epoch: 0015 cost= 0.031359817
Optimization Finished!
Accuracy: 0.0306308
Error after 0 iterations:0.0306308
Epoch: 0001 cost= 0.031828921
Epoch: 0002 cost= 0.031930040
Epoch: 0003 cost= 0.031385755
Epoch: 0004 cost= 0.030952285
Epoch: 0005 cost= 0.031673913
Epoch: 0006 cost= 0.031075206
Epoch: 0007 cost= 0.031009015
Epoch: 0008 cost= 0.031367686
Epoch: 0009 cost= 0.030735902
Epoch: 0010 cost= 0.031225719
Epoch: 0011 cost= 0.030860461
Epoch: 0012 cost= 0.030864338
Epoch: 0013 cost= 0.030237111
Epoch: 0014 cost= 0.030371800
Epoch: 0015 cost= 0.030202129
Optimization Finished!
Accuracy: 0.0297111
Error after 0 iterations:0.0297111
Final params:

In [47]:
#Connected components search

        
#Used for softening the training criteria.  There's some fuzz required due to the difference in 
#training error between test and training
thresh_multiplier = 1.1

    
results = []
tests = []

connecteddict = {}
for i1 in xrange(len(models)):
    connecteddict[i1] = 'not connected'


for i1 in xrange(len(models)):
    print i1
    for i2 in xrange(len(models)):
        
        if i2 > i1 and ((connecteddict[i1] != connecteddict[i2]) or (connecteddict[i1] == 'not connected' or connecteddict[i2] == 'not connected')) :
            #print "slow1?"
            #print i1,i2
            #print models[0]
            #print models[1]
            #print models[0].params
            #print models[1].params
            test = WeightString(models[i1].params[0],models[i1].params[1],models[i2].params[0],models[i2].params[1],1,1)

            training_threshold = thresh

            depth = 0
            d_max = 10

            #Check error between beads
            #Alg: for each bead at depth i, SGD until converged.
            #For beads with max error along path too large, add another bead between them, repeat

            
            #Keeps track of which indices to check the interpbeaderror between
            newindices = [0,1]
            
            while (depth < d_max):
                print newindices
                #print "slow2?"
                #X, y = GenTest(X,y)
                counter = 0

                for i,c in enumerate(test.ConvergedList):
                    if c == False:
                        #print "slow3?"
                        error = test.SGDBead(i, .5*training_threshold, 20)
                        #print "slow4?"
                            #if counter%5000==0:
                            #    print counter
                            #    print error
                        test.ConvergedList[i] = True

                print test.ConvergedList

                interperrors = []
                interp_bead_indices = []
                for b in xrange(len(test.AllBeads)-1):
                    if b in newindices:
                        e = InterpBeadError(test.AllBeads[b][0],test.AllBeads[b][1], test.AllBeads[b+1][0], test.AllBeads[b+1][1])

                        interperrors.append(e)
                        interp_bead_indices.append(b)
                print interperrors
                print "Interp bead indices: "
                print interp_bead_indices

                if max([ee[0] for ee in interperrors]) < thresh_multiplier*training_threshold:
                    depth = 2*d_max
                    #print test.ConvergedList
                    #print test.SpringNorm(2)
                    #print "Done!"

                else:
                    del newindices[:]
                    #Interperrors stores the maximum error on the path between beads
                    #shift index to account for added beads
                    shift = 0
                    for i, ie in enumerate(interperrors):
                        if ie[0] > thresh_multiplier*training_threshold:
                            k = interp_bead_indices[i]
                            
                            ws,bs = model_interpolate(test.AllBeads[k+shift][0],test.AllBeads[k+shift][1],\
                                                      test.AllBeads[k+shift+1][0],test.AllBeads[k+shift+1][1],\
                                                      ie[1]/100.)
                            
                            test.AllBeads.insert(k+shift+1,[ws,bs])
                            test.ConvergedList.insert(k+shift+1, False)
                            newindices.append(k+shift)
                            newindices.append(k+shift+1)
                            shift+=1
                            #print test.ConvergedList
                            #print test.SpringNorm(2)


                    #print d_max
                    depth += 1
            if depth == 2*d_max:
                results.append([i1,i2,test.SpringNorm(2),"Connected"])
                if connecteddict[i1] == 'not connected' and connecteddict[i2] == 'not connected':
                    connecteddict[i1] = i1
                    connecteddict[i2] = i1

                if connecteddict[i1] == 'not connected':
                    connecteddict[i1] = connecteddict[i2]
                else:
                    if connecteddict[i2] == 'not connected':
                        connecteddict[i2] = connecteddict[i1]
                    else:
                        if connecteddict[i1] != 'not connected' and connecteddict[i2] != 'not connected':
                            hold = connecteddict[i2]
                            connecteddict[i2] = connecteddict[i1]
                            for h in xrange(len(models)):
                                if connecteddict[h] == hold:
                                    connecteddict[h] = connecteddict[i1]
                    
            else:
                results.append([i1,i2,test.SpringNorm(2),"Disconnected"])
            #print results[-1]
            tests.append(test)
    

uniquecomps = []
totalcomps = 0
for i in xrange(len(models)):
    if not (connecteddict[i] in uniquecomps):
        uniquecomps.append(connecteddict[i])
    
    if connecteddict[i] == 'not connected':
        totalcomps += 1
        
    #print i,connecteddict[i]

notconoffset = 0

if 'not connected' in uniquecomps:
    notconoffset = -1
    
print "Thresh: " + str(thresh)
print "Comps: " + str(len(uniquecomps) + notconoffset + totalcomps)



#for i in xrange(len(synapses)):
#    print connecteddict[i]

connsum = []
for r in results:
    if r[3] == "Connected":
        connsum.append(r[2])
        #print r[2]
        
print "***"
print np.average(connsum)
print np.std(connsum)


0
[0, 1]
Accuracy: 0.0141108
Final bead error: 0.0141108
[True, True, True]
Accuracy: 0.0297765 	0
Accuracy: 0.031562 	1
Accuracy: 0.0349438 	2
Accuracy: 0.0400737 	3
Accuracy: 0.0469294 	4
Accuracy: 0.055473 	5
Accuracy: 0.0655853 	6
Accuracy: 0.0772344 	7
Accuracy: 0.0905034 	8
Accuracy: 0.105379 	9
Accuracy: 0.121862 	10
Accuracy: 0.139814 	11
Accuracy: 0.159099 	12
Accuracy: 0.179746 	13
Accuracy: 0.201743 	14
Accuracy: 0.224993 	15
Accuracy: 0.249388 	16
Accuracy: 0.274853 	17
Accuracy: 0.301352 	18
Accuracy: 0.328792 	19
Accuracy: 0.357109 	20
Accuracy: 0.386159 	21
Accuracy: 0.415949 	22
Accuracy: 0.446417 	23
Accuracy: 0.477417 	24
Accuracy: 0.508893 	25
Accuracy: 0.540697 	26
Accuracy: 0.572815 	27
Accuracy: 0.605149 	28
Accuracy: 0.637665 	29
Accuracy: 0.670133 	30
Accuracy: 0.702353 	31
Accuracy: 0.734242 	32
Accuracy: 0.765723 	33
Accuracy: 0.796727 	34
Accuracy: 0.827188 	35
Accuracy: 0.857047 	36
Accuracy: 0.88625 	37
Accuracy: 0.914749 	38
Accuracy: 0.942497 	39
Accuracy: 0.969456 	40
Accuracy: 0.995591 	41
Accuracy: 1.02087 	42
Accuracy: 1.04527 	43
Accuracy: 1.06876 	44
Accuracy: 1.09132 	45
Accuracy: 1.11295 	46
Accuracy: 1.13362 	47
Accuracy: 1.15333 	48
Accuracy: 1.17207 	49
Accuracy: 1.18984 	50
Accuracy: 1.20663 	51
Accuracy: 1.22246 	52
Accuracy: 1.22168 	53
Accuracy: 1.19704 	54
Accuracy: 1.17113 	55
Accuracy: 1.14402 	56
Accuracy: 1.11579 	57
Accuracy: 1.08651 	58
Accuracy: 1.05627 	59
Accuracy: 1.02516 	60
Accuracy: 0.993243 	61
Accuracy: 0.960619 	62
Accuracy: 0.92737 	63
Accuracy: 0.893581 	64
Accuracy: 0.859347 	65
Accuracy: 0.82476 	66
Accuracy: 0.789913 	67
Accuracy: 0.754891 	68
Accuracy: 0.719789 	69
Accuracy: 0.684691 	70
Accuracy: 0.649682 	71
Accuracy: 0.61485 	72
Accuracy: 0.580278 	73
Accuracy: 0.546057 	74
Accuracy: 0.512267 	75
Accuracy: 0.478991 	76
Accuracy: 0.446308 	77
Accuracy: 0.41429 	78
Accuracy: 0.383018 	79
Accuracy: 0.352569 	80
Accuracy: 0.32301 	81
Accuracy: 0.294412 	82
Accuracy: 0.266847 	83
Accuracy: 0.240381 	84
Accuracy: 0.215078 	85
Accuracy: 0.191003 	86
Accuracy: 0.168219 	87
Accuracy: 0.146789 	88
Accuracy: 0.126765 	89
Accuracy: 0.108206 	90
Accuracy: 0.0911623 	91
Accuracy: 0.0756858 	92
Accuracy: 0.0618248 	93
Accuracy: 0.0496274 	94
Accuracy: 0.0391368 	95
Accuracy: 0.0303955 	96
Accuracy: 0.0234425 	97
Accuracy: 0.0183162 	98
Accuracy: 0.0150518 	99
Accuracy: 0.0135378 	0
Accuracy: 0.0140632 	1
Accuracy: 0.0153402 	2
Accuracy: 0.0173452 	3
Accuracy: 0.0200535 	4
Accuracy: 0.0234391 	5
Accuracy: 0.0274738 	6
Accuracy: 0.0321171 	7
Accuracy: 0.0373357 	8
Accuracy: 0.0431048 	9
Accuracy: 0.0494026 	10
Accuracy: 0.0562066 	11
Accuracy: 0.0634935 	12
Accuracy: 0.0712424 	13
Accuracy: 0.079437 	14
Accuracy: 0.0880638 	15
Accuracy: 0.097114 	16
Accuracy: 0.106575 	17
Accuracy: 0.116411 	18
Accuracy: 0.126618 	19
Accuracy: 0.137179 	20
Accuracy: 0.148091 	21
Accuracy: 0.159328 	22
Accuracy: 0.170913 	23
Accuracy: 0.182787 	24
Accuracy: 0.194982 	25
Accuracy: 0.207489 	26
Accuracy: 0.220308 	27
Accuracy: 0.233478 	28
Accuracy: 0.246969 	29
Accuracy: 0.260815 	30
Accuracy: 0.274996 	31
Accuracy: 0.289221 	32
Accuracy: 0.303428 	33
Accuracy: 0.317578 	34
Accuracy: 0.331628 	35
Accuracy: 0.345539 	36
Accuracy: 0.359269 	37
Accuracy: 0.372775 	38
Accuracy: 0.386017 	39
Accuracy: 0.398957 	40
Accuracy: 0.41155 	41
Accuracy: 0.423753 	42
Accuracy: 0.435527 	43
Accuracy: 0.446833 	44
Accuracy: 0.457632 	45
Accuracy: 0.467889 	46
Accuracy: 0.477561 	47
Accuracy: 0.486614 	48
Accuracy: 0.495013 	49
Accuracy: 0.502721 	50
Accuracy: 0.509702 	51
Accuracy: 0.515921 	52
Accuracy: 0.521356 	53
Accuracy: 0.525972 	54
Accuracy: 0.529738 	55
Accuracy: 0.532635 	56
Accuracy: 0.534635 	57
Accuracy: 0.535728 	58
Accuracy: 0.535894 	59
Accuracy: 0.535117 	60
Accuracy: 0.533381 	61
Accuracy: 0.530677 	62
Accuracy: 0.526996 	63
Accuracy: 0.522327 	64
Accuracy: 0.516663 	65
Accuracy: 0.510002 	66
Accuracy: 0.502343 	67
Accuracy: 0.4937 	68
Accuracy: 0.484107 	69
Accuracy: 0.473567 	70
Accuracy: 0.462108 	71
Accuracy: 0.449763 	72
Accuracy: 0.436539 	73
Accuracy: 0.422472 	74
Accuracy: 0.407617 	75
Accuracy: 0.392026 	76
Accuracy: 0.375738 	77
Accuracy: 0.358795 	78
Accuracy: 0.34128 	79
Accuracy: 0.323267 	80
Accuracy: 0.304835 	81
Accuracy: 0.286063 	82
Accuracy: 0.267038 	83
Accuracy: 0.247852 	84
Accuracy: 0.228618 	85
Accuracy: 0.209435 	86
Accuracy: 0.190419 	87
Accuracy: 0.171706 	88
Accuracy: 0.153432 	89
Accuracy: 0.135737 	90
Accuracy: 0.118781 	91
Accuracy: 0.10273 	92
Accuracy: 0.0877537 	93
Accuracy: 0.0740367 	94
Accuracy: 0.0617751 	95
Accuracy: 0.0511752 	96
Accuracy: 0.0424457 	97
Accuracy: 0.0358068 	98
Accuracy: 0.0314941 	99
[(1.2224602, 52), (0.53589422, 59)]
Interp bead indices: 
[0, 1]
[0, 1, 2, 3]
Accuracy: 0.0375192
Accuracy: 0.0118964
Final bead error: 0.0118964
Accuracy: 0.0134421
Final bead error: 0.0134421
[True, True, True, True, True]
Accuracy: 0.028936 	0
Accuracy: 0.0286747 	1
Accuracy: 0.0284315 	2
Accuracy: 0.0282061 	3
Accuracy: 0.0279991 	4
Accuracy: 0.0278085 	5
Accuracy: 0.0276307 	6
Accuracy: 0.0274681 	7
Accuracy: 0.0273212 	8
Accuracy: 0.0271919 	9
Accuracy: 0.0270703 	10
Accuracy: 0.0269597 	11
Accuracy: 0.0268652 	12
Accuracy: 0.0267859 	13
Accuracy: 0.026714 	14
Accuracy: 0.0266507 	15
Accuracy: 0.0266013 	16
Accuracy: 0.0265645 	17
Accuracy: 0.0265381 	18
Accuracy: 0.0265188 	19
Accuracy: 0.0265031 	20
Accuracy: 0.0264903 	21
Accuracy: 0.0264844 	22
Accuracy: 0.0264889 	23
Accuracy: 0.026501 	24
Accuracy: 0.0265184 	25
Accuracy: 0.0265373 	26
Accuracy: 0.0265591 	27
Accuracy: 0.0265836 	28
Accuracy: 0.0266109 	29
Accuracy: 0.0266429 	30
Accuracy: 0.026681 	31
Accuracy: 0.0267261 	32
Accuracy: 0.0267755 	33
Accuracy: 0.0268318 	34
Accuracy: 0.0268881 	35
Accuracy: 0.0269453 	36
Accuracy: 0.0270066 	37
Accuracy: 0.0270694 	38
Accuracy: 0.0271324 	39
Accuracy: 0.027196 	40
Accuracy: 0.0272594 	41
Accuracy: 0.027321 	42
Accuracy: 0.0273794 	43
Accuracy: 0.0274342 	44
Accuracy: 0.0274854 	45
Accuracy: 0.0275317 	46
Accuracy: 0.0275728 	47
Accuracy: 0.0276082 	48
Accuracy: 0.0276369 	49
Accuracy: 0.0276584 	50
Accuracy: 0.0276724 	51
Accuracy: 0.0276776 	52
Accuracy: 0.0276739 	53
Accuracy: 0.027659 	54
Accuracy: 0.0276339 	55
Accuracy: 0.0275997 	56
Accuracy: 0.0275519 	57
Accuracy: 0.0274902 	58
Accuracy: 0.0274142 	59
Accuracy: 0.0273246 	60
Accuracy: 0.0272183 	61
Accuracy: 0.0270933 	62
Accuracy: 0.0269517 	63
Accuracy: 0.0267901 	64
Accuracy: 0.0266086 	65
Accuracy: 0.0264116 	66
Accuracy: 0.0261955 	67
Accuracy: 0.0259563 	68
Accuracy: 0.0256974 	69
Accuracy: 0.0254182 	70
Accuracy: 0.0251221 	71
Accuracy: 0.0248087 	72
Accuracy: 0.0244698 	73
Accuracy: 0.0241043 	74
Accuracy: 0.0237144 	75
Accuracy: 0.0233018 	76
Accuracy: 0.0228692 	77
Accuracy: 0.0224173 	78
Accuracy: 0.021945 	79
Accuracy: 0.0214461 	80
Accuracy: 0.0209218 	81
Accuracy: 0.0203794 	82
Accuracy: 0.0198445 	83
Accuracy: 0.0193231 	84
Accuracy: 0.0188147 	85
Accuracy: 0.018319 	86
Accuracy: 0.0178354 	87
Accuracy: 0.0173635 	88
Accuracy: 0.0169029 	89
Accuracy: 0.0164533 	90
Accuracy: 0.0160141 	91
Accuracy: 0.0155851 	92
Accuracy: 0.015166 	93
Accuracy: 0.0147563 	94
Accuracy: 0.0143559 	95
Accuracy: 0.0139645 	96
Accuracy: 0.0135818 	97
Accuracy: 0.0132074 	98
Accuracy: 0.0128414 	99
Accuracy: 0.0127498 	0
Accuracy: 0.0138134 	1
Accuracy: 0.0160641 	2
Accuracy: 0.0194453 	3
Accuracy: 0.0239024 	4
Accuracy: 0.029382 	5
Accuracy: 0.0358324 	6
Accuracy: 0.0432033 	7
Accuracy: 0.0514456 	8
Accuracy: 0.060512 	9
Accuracy: 0.0703563 	10
Accuracy: 0.0809337 	11
Accuracy: 0.0922009 	12
Accuracy: 0.104106 	13
Accuracy: 0.116478 	14
Accuracy: 0.129167 	15
Accuracy: 0.142055 	16
Accuracy: 0.155083 	17
Accuracy: 0.168151 	18
Accuracy: 0.18113 	19
Accuracy: 0.193923 	20
Accuracy: 0.20643 	21
Accuracy: 0.218505 	22
Accuracy: 0.230016 	23
Accuracy: 0.240799 	24
Accuracy: 0.250591 	25
Accuracy: 0.259071 	26
Accuracy: 0.26628 	27
Accuracy: 0.272116 	28
Accuracy: 0.276117 	29
Accuracy: 0.27936 	30
Accuracy: 0.282243 	31
Accuracy: 0.284764 	32
Accuracy: 0.286921 	33
Accuracy: 0.28871 	34
Accuracy: 0.290133 	35
Accuracy: 0.291189 	36
Accuracy: 0.291879 	37
Accuracy: 0.292204 	38
Accuracy: 0.292168 	39
Accuracy: 0.291773 	40
Accuracy: 0.291023 	41
Accuracy: 0.289923 	42
Accuracy: 0.288478 	43
Accuracy: 0.286694 	44
Accuracy: 0.284576 	45
Accuracy: 0.282133 	46
Accuracy: 0.279372 	47
Accuracy: 0.2763 	48
Accuracy: 0.272928 	49
Accuracy: 0.269263 	50
Accuracy: 0.265315 	51
Accuracy: 0.261096 	52
Accuracy: 0.256614 	53
Accuracy: 0.251882 	54
Accuracy: 0.246911 	55
Accuracy: 0.241712 	56
Accuracy: 0.236298 	57
Accuracy: 0.230682 	58
Accuracy: 0.224877 	59
Accuracy: 0.218895 	60
Accuracy: 0.212751 	61
Accuracy: 0.206458 	62
Accuracy: 0.20003 	63
Accuracy: 0.193482 	64
Accuracy: 0.186829 	65
Accuracy: 0.180085 	66
Accuracy: 0.173266 	67
Accuracy: 0.166386 	68
Accuracy: 0.159461 	69
Accuracy: 0.152508 	70
Accuracy: 0.145541 	71
Accuracy: 0.138576 	72
Accuracy: 0.13163 	73
Accuracy: 0.124721 	74
Accuracy: 0.11788 	75
Accuracy: 0.111134 	76
Accuracy: 0.104505 	77
Accuracy: 0.0980068 	78
Accuracy: 0.0916559 	79
Accuracy: 0.085458 	80
Accuracy: 0.0794262 	81
Accuracy: 0.0735683 	82
Accuracy: 0.0678973 	83
Accuracy: 0.0624326 	84
Accuracy: 0.0571905 	85
Accuracy: 0.052176 	86
Accuracy: 0.0474026 	87
Accuracy: 0.0428882 	88
Accuracy: 0.0386473 	89
Accuracy: 0.0346963 	90
Accuracy: 0.0310439 	91
Accuracy: 0.0276993 	92
Accuracy: 0.0246764 	93
Accuracy: 0.0219881 	94
Accuracy: 0.0196502 	95
Accuracy: 0.0176742 	96
Accuracy: 0.0160727 	97
Accuracy: 0.0148591 	98
Accuracy: 0.0140445 	99
Accuracy: 0.0139125 	0
Accuracy: 0.013939 	1
Accuracy: 0.0140888 	2
Accuracy: 0.0143557 	3
Accuracy: 0.0147337 	4
Accuracy: 0.0152165 	5
Accuracy: 0.0157979 	6
Accuracy: 0.0164716 	7
Accuracy: 0.0172314 	8
Accuracy: 0.0180709 	9
Accuracy: 0.0189839 	10
Accuracy: 0.0199582 	11
Accuracy: 0.0209831 	12
Accuracy: 0.0220589 	13
Accuracy: 0.0231771 	14
Accuracy: 0.0243317 	15
Accuracy: 0.0255181 	16
Accuracy: 0.0267307 	17
Accuracy: 0.0279657 	18
Accuracy: 0.0292187 	19
Accuracy: 0.0304853 	20
Accuracy: 0.0317617 	21
Accuracy: 0.0330446 	22
Accuracy: 0.0343301 	23
Accuracy: 0.0356141 	24
Accuracy: 0.0368949 	25
Accuracy: 0.0381697 	26
Accuracy: 0.0394333 	27
Accuracy: 0.0406822 	28
Accuracy: 0.0419106 	29
Accuracy: 0.0431171 	30
Accuracy: 0.0443017 	31
Accuracy: 0.0454634 	32
Accuracy: 0.0466029 	33
Accuracy: 0.0477125 	34
Accuracy: 0.0487875 	35
Accuracy: 0.0498302 	36
Accuracy: 0.0508435 	37
Accuracy: 0.051831 	38
Accuracy: 0.0527866 	39
Accuracy: 0.0537095 	40
Accuracy: 0.0546064 	41
Accuracy: 0.0554606 	42
Accuracy: 0.0562814 	43
Accuracy: 0.0570567 	44
Accuracy: 0.0577873 	45
Accuracy: 0.0584773 	46
Accuracy: 0.0591233 	47
Accuracy: 0.0597238 	48
Accuracy: 0.0602845 	49
Accuracy: 0.0608103 	50
Accuracy: 0.0613049 	51
Accuracy: 0.0617678 	52
Accuracy: 0.0621894 	53
Accuracy: 0.0625243 	54
Accuracy: 0.0627697 	55
Accuracy: 0.0629231 	56
Accuracy: 0.0629842 	57
Accuracy: 0.0629511 	58
Accuracy: 0.0628236 	59
Accuracy: 0.0626004 	60
Accuracy: 0.0622809 	61
Accuracy: 0.0618655 	62
Accuracy: 0.061354 	63
Accuracy: 0.0607468 	64
Accuracy: 0.0600446 	65
Accuracy: 0.0592483 	66
Accuracy: 0.0583603 	67
Accuracy: 0.057382 	68
Accuracy: 0.056316 	69
Accuracy: 0.0551649 	70
Accuracy: 0.0539318 	71
Accuracy: 0.0526196 	72
Accuracy: 0.0512328 	73
Accuracy: 0.0497756 	74
Accuracy: 0.0482527 	75
Accuracy: 0.0466685 	76
Accuracy: 0.045029 	77
Accuracy: 0.0433403 	78
Accuracy: 0.0416086 	79
Accuracy: 0.0398417 	80
Accuracy: 0.0380468 	81
Accuracy: 0.036232 	82
Accuracy: 0.0344062 	83
Accuracy: 0.0325783 	84
Accuracy: 0.0307584 	85
Accuracy: 0.0289566 	86
Accuracy: 0.0271842 	87
Accuracy: 0.0254525 	88
Accuracy: 0.023774 	89
Accuracy: 0.0221614 	90
Accuracy: 0.0206296 	91
Accuracy: 0.0191917 	92
Accuracy: 0.0178635 	93
Accuracy: 0.0166612 	94
Accuracy: 0.015601 	95
Accuracy: 0.0146984 	96
Accuracy: 0.0139729 	97
Accuracy: 0.0134436 	98
Accuracy: 0.0131301 	99
Accuracy: 0.0131809 	0
Accuracy: 0.0133396 	1
Accuracy: 0.0135305 	2
Accuracy: 0.0137522 	3
Accuracy: 0.0140031 	4
Accuracy: 0.0142819 	5
Accuracy: 0.0145872 	6
Accuracy: 0.0149174 	7
Accuracy: 0.0152712 	8
Accuracy: 0.015647 	9
Accuracy: 0.0160437 	10
Accuracy: 0.0164599 	11
Accuracy: 0.0168942 	12
Accuracy: 0.0173451 	13
Accuracy: 0.0178111 	14
Accuracy: 0.0182909 	15
Accuracy: 0.0187832 	16
Accuracy: 0.0192866 	17
Accuracy: 0.0197999 	18
Accuracy: 0.0203218 	19
Accuracy: 0.0208508 	20
Accuracy: 0.0213856 	21
Accuracy: 0.021925 	22
Accuracy: 0.0224676 	23
Accuracy: 0.0230123 	24
Accuracy: 0.0235579 	25
Accuracy: 0.0241034 	26
Accuracy: 0.0246472 	27
Accuracy: 0.0251882 	28
Accuracy: 0.0257254 	29
Accuracy: 0.0262578 	30
Accuracy: 0.0267843 	31
Accuracy: 0.0273038 	32
Accuracy: 0.0278157 	33
Accuracy: 0.0283188 	34
Accuracy: 0.0288117 	35
Accuracy: 0.0292935 	36
Accuracy: 0.0297631 	37
Accuracy: 0.0302196 	38
Accuracy: 0.0306626 	39
Accuracy: 0.0310915 	40
Accuracy: 0.0315052 	41
Accuracy: 0.0319028 	42
Accuracy: 0.0322836 	43
Accuracy: 0.032647 	44
Accuracy: 0.0329923 	45
Accuracy: 0.0333187 	46
Accuracy: 0.033626 	47
Accuracy: 0.0339137 	48
Accuracy: 0.0341816 	49
Accuracy: 0.0344289 	50
Accuracy: 0.0346551 	51
Accuracy: 0.0348603 	52
Accuracy: 0.0350446 	53
Accuracy: 0.0352071 	54
Accuracy: 0.0353472 	55
Accuracy: 0.0354652 	56
Accuracy: 0.0355608 	57
Accuracy: 0.0356343 	58
Accuracy: 0.0356858 	59
Accuracy: 0.0357159 	60
Accuracy: 0.035724 	61
Accuracy: 0.0357109 	62
Accuracy: 0.0356765 	63
Accuracy: 0.035621 	64
Accuracy: 0.0355453 	65
Accuracy: 0.0354502 	66
Accuracy: 0.0353362 	67
Accuracy: 0.0352037 	68
Accuracy: 0.0350532 	69
Accuracy: 0.0348864 	70
Accuracy: 0.0347043 	71
Accuracy: 0.0345072 	72
Accuracy: 0.0342967 	73
Accuracy: 0.0340738 	74
Accuracy: 0.0338391 	75
Accuracy: 0.0335938 	76
Accuracy: 0.0333396 	77
Accuracy: 0.0330773 	78
Accuracy: 0.0328082 	79
Accuracy: 0.0325338 	80
Accuracy: 0.0322565 	81
Accuracy: 0.031978 	82
Accuracy: 0.0317001 	83
Accuracy: 0.0314243 	84
Accuracy: 0.0311544 	85
Accuracy: 0.0308928 	86
Accuracy: 0.0306403 	87
Accuracy: 0.0304015 	88
Accuracy: 0.0301776 	89
Accuracy: 0.0299698 	90
Accuracy: 0.0297812 	91
Accuracy: 0.0296146 	92
Accuracy: 0.0294728 	93
Accuracy: 0.0293581 	94
Accuracy: 0.0292738 	95
Accuracy: 0.0292235 	96
Accuracy: 0.0292122 	97
Accuracy: 0.0292439 	98
Accuracy: 0.0293233 	99
[(0.02893603, 0), (0.29220423, 38), (0.062984161, 57), (0.035724018, 61)]
Interp bead indices: 
[0, 1, 2, 3]
[1, 2, 3, 4, 5, 6]
Accuracy: 0.0146754
Final bead error: 0.0146754
Accuracy: 0.00920634
Final bead error: 0.00920634
Accuracy: 0.010424
Final bead error: 0.010424
[True, True, True, True, True, True, True, True]
Accuracy: 0.0131544 	0
Accuracy: 0.0133263 	1
Accuracy: 0.0135704 	2
Accuracy: 0.0138848 	3
Accuracy: 0.0142675 	4
Accuracy: 0.0147165 	5
Accuracy: 0.01523 	6
Accuracy: 0.0158059 	7
Accuracy: 0.0164425 	8
Accuracy: 0.0171379 	9
Accuracy: 0.0178903 	10
Accuracy: 0.0186979 	11
Accuracy: 0.0195588 	12
Accuracy: 0.0204715 	13
Accuracy: 0.0214341 	14
Accuracy: 0.0224451 	15
Accuracy: 0.0235026 	16
Accuracy: 0.024605 	17
Accuracy: 0.0257509 	18
Accuracy: 0.0269384 	19
Accuracy: 0.0281662 	20
Accuracy: 0.0294327 	21
Accuracy: 0.0307362 	22
Accuracy: 0.0320754 	23
Accuracy: 0.0334488 	24
Accuracy: 0.0348548 	25
Accuracy: 0.0362922 	26
Accuracy: 0.0377594 	27
Accuracy: 0.0392495 	28
Accuracy: 0.0407521 	29
Accuracy: 0.0422667 	30
Accuracy: 0.0437874 	31
Accuracy: 0.0453105 	32
Accuracy: 0.0468304 	33
Accuracy: 0.0483451 	34
Accuracy: 0.0498507 	35
Accuracy: 0.0513437 	36
Accuracy: 0.0528102 	37
Accuracy: 0.0542428 	38
Accuracy: 0.0556349 	39
Accuracy: 0.0569766 	40
Accuracy: 0.0582591 	41
Accuracy: 0.0594776 	42
Accuracy: 0.0606291 	43
Accuracy: 0.0616991 	44
Accuracy: 0.0626719 	45
Accuracy: 0.0635059 	46
Accuracy: 0.0641655 	47
Accuracy: 0.0646595 	48
Accuracy: 0.0649589 	49
Accuracy: 0.0650117 	50
Accuracy: 0.0647993 	51
Accuracy: 0.0642944 	52
Accuracy: 0.0634049 	53
Accuracy: 0.0622773 	54
Accuracy: 0.0611309 	55
Accuracy: 0.0599686 	56
Accuracy: 0.0587916 	57
Accuracy: 0.0576011 	58
Accuracy: 0.0563985 	59
Accuracy: 0.0551849 	60
Accuracy: 0.0539616 	61
Accuracy: 0.0527299 	62
Accuracy: 0.0514911 	63
Accuracy: 0.0502467 	64
Accuracy: 0.0489979 	65
Accuracy: 0.0477463 	66
Accuracy: 0.0464932 	67
Accuracy: 0.04524 	68
Accuracy: 0.0439883 	69
Accuracy: 0.0427396 	70
Accuracy: 0.0414954 	71
Accuracy: 0.0402571 	72
Accuracy: 0.0390265 	73
Accuracy: 0.0378051 	74
Accuracy: 0.0365945 	75
Accuracy: 0.0353963 	76
Accuracy: 0.0342123 	77
Accuracy: 0.033044 	78
Accuracy: 0.0318932 	79
Accuracy: 0.0307616 	80
Accuracy: 0.029651 	81
Accuracy: 0.0285631 	82
Accuracy: 0.0274997 	83
Accuracy: 0.0264626 	84
Accuracy: 0.0254536 	85
Accuracy: 0.0244747 	86
Accuracy: 0.0235276 	87
Accuracy: 0.0226142 	88
Accuracy: 0.0217365 	89
Accuracy: 0.0208964 	90
Accuracy: 0.0200958 	91
Accuracy: 0.0193367 	92
Accuracy: 0.018621 	93
Accuracy: 0.0179508 	94
Accuracy: 0.0173281 	95
Accuracy: 0.0167549 	96
Accuracy: 0.0162334 	97
Accuracy: 0.0157655 	98
Accuracy: 0.0153533 	99
Accuracy: 0.0137387 	0
Accuracy: 0.0138952 	1
Accuracy: 0.0140927 	2
Accuracy: 0.0143277 	3
Accuracy: 0.0145968 	4
Accuracy: 0.0148967 	5
Accuracy: 0.0152244 	6
Accuracy: 0.0155767 	7
Accuracy: 0.0159507 	8
Accuracy: 0.0163437 	9
Accuracy: 0.0167529 	10
Accuracy: 0.0171756 	11
Accuracy: 0.0176094 	12
Accuracy: 0.018052 	13
Accuracy: 0.0185008 	14
Accuracy: 0.0189539 	15
Accuracy: 0.019409 	16
Accuracy: 0.0198641 	17
Accuracy: 0.0203173 	18
Accuracy: 0.0207668 	19
Accuracy: 0.0212109 	20
Accuracy: 0.0216478 	21
Accuracy: 0.0220761 	22
Accuracy: 0.0224942 	23
Accuracy: 0.0229008 	24
Accuracy: 0.0232945 	25
Accuracy: 0.0236742 	26
Accuracy: 0.0240386 	27
Accuracy: 0.0243867 	28
Accuracy: 0.0247176 	29
Accuracy: 0.0250303 	30
Accuracy: 0.025324 	31
Accuracy: 0.0255978 	32
Accuracy: 0.0258531 	33
Accuracy: 0.0260896 	34
Accuracy: 0.0263068 	35
Accuracy: 0.0265044 	36
Accuracy: 0.0266821 	37
Accuracy: 0.026838 	38
Accuracy: 0.0269716 	39
Accuracy: 0.0270833 	40
Accuracy: 0.0271729 	41
Accuracy: 0.0272412 	42
Accuracy: 0.0272882 	43
Accuracy: 0.0273133 	44
Accuracy: 0.0273176 	45
Accuracy: 0.0273015 	46
Accuracy: 0.0272665 	47
Accuracy: 0.0272095 	48
Accuracy: 0.0271314 	49
Accuracy: 0.0270326 	50
Accuracy: 0.0269141 	51
Accuracy: 0.0267788 	52
Accuracy: 0.0266262 	53
Accuracy: 0.0264566 	54
Accuracy: 0.0262684 	55
Accuracy: 0.0260622 	56
Accuracy: 0.0258398 	57
Accuracy: 0.0256013 	58
Accuracy: 0.0253466 	59
Accuracy: 0.0250764 	60
Accuracy: 0.0247911 	61
Accuracy: 0.0244911 	62
Accuracy: 0.0241771 	63
Accuracy: 0.0238512 	64
Accuracy: 0.0235135 	65
Accuracy: 0.0231651 	66
Accuracy: 0.0228072 	67
Accuracy: 0.0224409 	68
Accuracy: 0.0220658 	69
Accuracy: 0.021683 	70
Accuracy: 0.0212933 	71
Accuracy: 0.0208984 	72
Accuracy: 0.0205003 	73
Accuracy: 0.0200995 	74
Accuracy: 0.0196973 	75
Accuracy: 0.0192956 	76
Accuracy: 0.0188943 	77
Accuracy: 0.0184941 	78
Accuracy: 0.0180966 	79
Accuracy: 0.0177028 	80
Accuracy: 0.0173143 	81
Accuracy: 0.0169322 	82
Accuracy: 0.0165577 	83
Accuracy: 0.0161917 	84
Accuracy: 0.0158363 	85
Accuracy: 0.0154928 	86
Accuracy: 0.0151618 	87
Accuracy: 0.0148443 	88
Accuracy: 0.0145416 	89
Accuracy: 0.0142555 	90
Accuracy: 0.0139872 	91
Accuracy: 0.013738 	92
Accuracy: 0.0135097 	93
Accuracy: 0.0133033 	94
Accuracy: 0.0131203 	95
Accuracy: 0.0129618 	96
Accuracy: 0.0128293 	97
Accuracy: 0.012724 	98
Accuracy: 0.0126471 	99
Accuracy: 0.014121 	0
Accuracy: 0.0140783 	1
Accuracy: 0.0140532 	2
Accuracy: 0.0140449 	3
Accuracy: 0.0140523 	4
Accuracy: 0.0140745 	5
Accuracy: 0.0141107 	6
Accuracy: 0.0141599 	7
Accuracy: 0.0142211 	8
Accuracy: 0.0142935 	9
Accuracy: 0.0143761 	10
Accuracy: 0.0144682 	11
Accuracy: 0.0145688 	12
Accuracy: 0.014677 	13
Accuracy: 0.0147921 	14
Accuracy: 0.0149132 	15
Accuracy: 0.0150395 	16
Accuracy: 0.0151701 	17
Accuracy: 0.0153043 	18
Accuracy: 0.0154396 	19
Accuracy: 0.0155741 	20
Accuracy: 0.0157065 	21
Accuracy: 0.0158361 	22
Accuracy: 0.0159636 	23
Accuracy: 0.0160878 	24
Accuracy: 0.0162083 	25
Accuracy: 0.016323 	26
Accuracy: 0.0164332 	27
Accuracy: 0.0165379 	28
Accuracy: 0.0166366 	29
Accuracy: 0.0167285 	30
Accuracy: 0.0168136 	31
Accuracy: 0.0168913 	32
Accuracy: 0.0169614 	33
Accuracy: 0.0170236 	34
Accuracy: 0.0170777 	35
Accuracy: 0.0171237 	36
Accuracy: 0.0171611 	37
Accuracy: 0.0171896 	38
Accuracy: 0.0172092 	39
Accuracy: 0.0172195 	40
Accuracy: 0.0172205 	41
Accuracy: 0.0172119 	42
Accuracy: 0.0171939 	43
Accuracy: 0.0171661 	44
Accuracy: 0.0171285 	45
Accuracy: 0.0170809 	46
Accuracy: 0.0170234 	47
Accuracy: 0.0169559 	48
Accuracy: 0.0168784 	49
Accuracy: 0.0167907 	50
Accuracy: 0.0166929 	51
Accuracy: 0.0165849 	52
Accuracy: 0.0164686 	53
Accuracy: 0.0163435 	54
Accuracy: 0.0162096 	55
Accuracy: 0.0160685 	56
Accuracy: 0.0159194 	57
Accuracy: 0.0157622 	58
Accuracy: 0.0155983 	59
Accuracy: 0.0154269 	60
Accuracy: 0.015248 	61
Accuracy: 0.0150626 	62
Accuracy: 0.0148708 	63
Accuracy: 0.0146734 	64
Accuracy: 0.01447 	65
Accuracy: 0.014262 	66
Accuracy: 0.0140492 	67
Accuracy: 0.0138313 	68
Accuracy: 0.013609 	69
Accuracy: 0.0133838 	70
Accuracy: 0.0131546 	71
Accuracy: 0.0129217 	72
Accuracy: 0.0126878 	73
Accuracy: 0.012454 	74
Accuracy: 0.0122212 	75
Accuracy: 0.0119888 	76
Accuracy: 0.0117569 	77
Accuracy: 0.0115268 	78
Accuracy: 0.0112991 	79
Accuracy: 0.0110745 	80
Accuracy: 0.0108539 	81
Accuracy: 0.0106357 	82
Accuracy: 0.0104217 	83
Accuracy: 0.0102125 	84
Accuracy: 0.0100098 	85
Accuracy: 0.00981385 	86
Accuracy: 0.00962579 	87
Accuracy: 0.00944694 	88
Accuracy: 0.00927819 	89
Accuracy: 0.00911833 	90
Accuracy: 0.00897102 	91
Accuracy: 0.00883628 	92
Accuracy: 0.00871432 	93
Accuracy: 0.0086061 	94
Accuracy: 0.00850878 	95
Accuracy: 0.00842275 	96
Accuracy: 0.00834914 	97
Accuracy: 0.00828926 	98
Accuracy: 0.00824475 	99
Accuracy: 0.00777537 	0
Accuracy: 0.0078309 	1
Accuracy: 0.00789198 	2
Accuracy: 0.00795831 	3
Accuracy: 0.00802961 	4
Accuracy: 0.0081056 	5
Accuracy: 0.00818603 	6
Accuracy: 0.00827059 	7
Accuracy: 0.00835903 	8
Accuracy: 0.00845109 	9
Accuracy: 0.00854648 	10
Accuracy: 0.00864499 	11
Accuracy: 0.00874629 	12
Accuracy: 0.00885018 	13
Accuracy: 0.00895638 	14
Accuracy: 0.00906466 	15
Accuracy: 0.00917477 	16
Accuracy: 0.00928646 	17
Accuracy: 0.00939952 	18
Accuracy: 0.0095137 	19
Accuracy: 0.00962876 	20
Accuracy: 0.00974448 	21
Accuracy: 0.00986067 	22
Accuracy: 0.00997706 	23
Accuracy: 0.0100935 	24
Accuracy: 0.0102097 	25
Accuracy: 0.0103255 	26
Accuracy: 0.0104407 	27
Accuracy: 0.0105551 	28
Accuracy: 0.0106686 	29
Accuracy: 0.0107809 	30
Accuracy: 0.0108919 	31
Accuracy: 0.0110013 	32
Accuracy: 0.0111091 	33
Accuracy: 0.0112151 	34
Accuracy: 0.0113191 	35
Accuracy: 0.0114209 	36
Accuracy: 0.0115205 	37
Accuracy: 0.0116176 	38
Accuracy: 0.0117123 	39
Accuracy: 0.0118042 	40
Accuracy: 0.0118933 	41
Accuracy: 0.0119796 	42
Accuracy: 0.0120628 	43
Accuracy: 0.012143 	44
Accuracy: 0.01222 	45
Accuracy: 0.0122937 	46
Accuracy: 0.012364 	47
Accuracy: 0.0124309 	48
Accuracy: 0.0124944 	49
Accuracy: 0.0125543 	50
Accuracy: 0.0126107 	51
Accuracy: 0.0126635 	52
Accuracy: 0.0127126 	53
Accuracy: 0.0127581 	54
Accuracy: 0.0127999 	55
Accuracy: 0.0128381 	56
Accuracy: 0.0128726 	57
Accuracy: 0.0129035 	58
Accuracy: 0.0129308 	59
Accuracy: 0.0129546 	60
Accuracy: 0.0129749 	61
Accuracy: 0.0129919 	62
Accuracy: 0.0130053 	63
Accuracy: 0.0130155 	64
Accuracy: 0.0130223 	65
Accuracy: 0.0130259 	66
Accuracy: 0.0130265 	67
Accuracy: 0.0130242 	68
Accuracy: 0.013019 	69
Accuracy: 0.0130111 	70
Accuracy: 0.0130007 	71
Accuracy: 0.0129878 	72
Accuracy: 0.0129727 	73
Accuracy: 0.0129555 	74
Accuracy: 0.0129364 	75
Accuracy: 0.0129156 	76
Accuracy: 0.0128936 	77
Accuracy: 0.0128703 	78
Accuracy: 0.0128461 	79
Accuracy: 0.0128213 	80
Accuracy: 0.012796 	81
Accuracy: 0.0127707 	82
Accuracy: 0.0127456 	83
Accuracy: 0.0127211 	84
Accuracy: 0.0126973 	85
Accuracy: 0.0126747 	86
Accuracy: 0.0126537 	87
Accuracy: 0.0126345 	88
Accuracy: 0.0126176 	89
Accuracy: 0.0126037 	90
Accuracy: 0.0125929 	91
Accuracy: 0.0125854 	92
Accuracy: 0.0125818 	93
Accuracy: 0.0125826 	94
Accuracy: 0.0125881 	95
Accuracy: 0.0125986 	96
Accuracy: 0.0126148 	97
Accuracy: 0.0126372 	98
Accuracy: 0.0126664 	99
Accuracy: 0.0136726 	0
Accuracy: 0.0136621 	1
Accuracy: 0.0136557 	2
Accuracy: 0.0136531 	3
Accuracy: 0.0136543 	4
Accuracy: 0.0136593 	5
Accuracy: 0.0136673 	6
Accuracy: 0.0136783 	7
Accuracy: 0.0136921 	8
Accuracy: 0.0137084 	9
Accuracy: 0.0137269 	10
Accuracy: 0.0137475 	11
Accuracy: 0.0137699 	12
Accuracy: 0.0137941 	13
Accuracy: 0.0138199 	14
Accuracy: 0.0138469 	15
Accuracy: 0.013875 	16
Accuracy: 0.013904 	17
Accuracy: 0.0139338 	18
Accuracy: 0.0139641 	19
Accuracy: 0.0139949 	20
Accuracy: 0.0140262 	21
Accuracy: 0.0140575 	22
Accuracy: 0.0140888 	23
Accuracy: 0.0141195 	24
Accuracy: 0.0141495 	25
Accuracy: 0.0141786 	26
Accuracy: 0.0142068 	27
Accuracy: 0.0142337 	28
Accuracy: 0.0142593 	29
Accuracy: 0.0142835 	30
Accuracy: 0.014306 	31
Accuracy: 0.014327 	32
Accuracy: 0.0143463 	33
Accuracy: 0.0143641 	34
Accuracy: 0.01438 	35
Accuracy: 0.0143936 	36
Accuracy: 0.014405 	37
Accuracy: 0.014414 	38
Accuracy: 0.0144205 	39
Accuracy: 0.0144247 	40
Accuracy: 0.0144265 	41
Accuracy: 0.0144255 	42
Accuracy: 0.0144218 	43
Accuracy: 0.0144151 	44
Accuracy: 0.0144053 	45
Accuracy: 0.0143925 	46
Accuracy: 0.0143767 	47
Accuracy: 0.0143577 	48
Accuracy: 0.0143357 	49
Accuracy: 0.0143104 	50
Accuracy: 0.0142825 	51
Accuracy: 0.0142516 	52
Accuracy: 0.0142176 	53
Accuracy: 0.0141807 	54
Accuracy: 0.0141409 	55
Accuracy: 0.0140982 	56
Accuracy: 0.0140523 	57
Accuracy: 0.0140034 	58
Accuracy: 0.013952 	59
Accuracy: 0.013898 	60
Accuracy: 0.0138408 	61
Accuracy: 0.0137808 	62
Accuracy: 0.0137179 	63
Accuracy: 0.0136528 	64
Accuracy: 0.013585 	65
Accuracy: 0.0135148 	66
Accuracy: 0.0134421 	67
Accuracy: 0.0133669 	68
Accuracy: 0.0132892 	69
Accuracy: 0.013209 	70
Accuracy: 0.0131267 	71
Accuracy: 0.0130423 	72
Accuracy: 0.0129564 	73
Accuracy: 0.0128694 	74
Accuracy: 0.0127808 	75
Accuracy: 0.0126909 	76
Accuracy: 0.0126003 	77
Accuracy: 0.0125092 	78
Accuracy: 0.0124174 	79
Accuracy: 0.0123252 	80
Accuracy: 0.0122331 	81
Accuracy: 0.012141 	82
Accuracy: 0.0120492 	83
Accuracy: 0.0119582 	84
Accuracy: 0.0118683 	85
Accuracy: 0.0117805 	86
Accuracy: 0.0116943 	87
Accuracy: 0.0116101 	88
Accuracy: 0.0115278 	89
Accuracy: 0.0114478 	90
Accuracy: 0.0113712 	91
Accuracy: 0.0112984 	92
Accuracy: 0.011229 	93
Accuracy: 0.0111638 	94
Accuracy: 0.0111033 	95
Accuracy: 0.0110473 	96
Accuracy: 0.0109962 	97
Accuracy: 0.0109521 	98
Accuracy: 0.0109147 	99
Accuracy: 0.0102219 	0
Accuracy: 0.0103512 	1
Accuracy: 0.0104827 	2
Accuracy: 0.0106164 	3
Accuracy: 0.0107522 	4
Accuracy: 0.01089 	5
Accuracy: 0.0110298 	6
Accuracy: 0.0111715 	7
Accuracy: 0.0113151 	8
Accuracy: 0.0114607 	9
Accuracy: 0.0116081 	10
Accuracy: 0.0117571 	11
Accuracy: 0.011908 	12
Accuracy: 0.0120604 	13
Accuracy: 0.0122144 	14
Accuracy: 0.01237 	15
Accuracy: 0.0125272 	16
Accuracy: 0.0126858 	17
Accuracy: 0.012846 	18
Accuracy: 0.0130075 	19
Accuracy: 0.0131704 	20
Accuracy: 0.0133347 	21
Accuracy: 0.0135003 	22
Accuracy: 0.0136672 	23
Accuracy: 0.0138353 	24
Accuracy: 0.0140045 	25
Accuracy: 0.0141751 	26
Accuracy: 0.0143468 	27
Accuracy: 0.0145197 	28
Accuracy: 0.0146937 	29
Accuracy: 0.0148686 	30
Accuracy: 0.0150446 	31
Accuracy: 0.0152215 	32
Accuracy: 0.0153994 	33
Accuracy: 0.0155782 	34
Accuracy: 0.015758 	35
Accuracy: 0.0159387 	36
Accuracy: 0.0161202 	37
Accuracy: 0.0163026 	38
Accuracy: 0.0164857 	39
Accuracy: 0.0166696 	40
Accuracy: 0.0168543 	41
Accuracy: 0.0170397 	42
Accuracy: 0.0172259 	43
Accuracy: 0.0174129 	44
Accuracy: 0.0176005 	45
Accuracy: 0.0177889 	46
Accuracy: 0.017978 	47
Accuracy: 0.0181677 	48
Accuracy: 0.0183581 	49
Accuracy: 0.0185492 	50
Accuracy: 0.0187409 	51
Accuracy: 0.0189332 	52
Accuracy: 0.0191262 	53
Accuracy: 0.0193198 	54
Accuracy: 0.0195141 	55
Accuracy: 0.0197092 	56
Accuracy: 0.0199051 	57
Accuracy: 0.0201016 	58
Accuracy: 0.0202988 	59
Accuracy: 0.0204967 	60
Accuracy: 0.0206953 	61
Accuracy: 0.0208945 	62
Accuracy: 0.0210944 	63
Accuracy: 0.021295 	64
Accuracy: 0.0214964 	65
Accuracy: 0.0216984 	66
Accuracy: 0.0219013 	67
Accuracy: 0.0221048 	68
Accuracy: 0.0223092 	69
Accuracy: 0.0225143 	70
Accuracy: 0.0227203 	71
Accuracy: 0.0229271 	72
Accuracy: 0.0231349 	73
Accuracy: 0.0233436 	74
Accuracy: 0.0235533 	75
Accuracy: 0.023764 	76
Accuracy: 0.0239756 	77
Accuracy: 0.0241884 	78
Accuracy: 0.0244024 	79
Accuracy: 0.0246174 	80
Accuracy: 0.0248334 	81
Accuracy: 0.0250507 	82
Accuracy: 0.0252692 	83
Accuracy: 0.0254889 	84
Accuracy: 0.0257099 	85
Accuracy: 0.0259322 	86
Accuracy: 0.026156 	87
Accuracy: 0.0263811 	88
Accuracy: 0.0266078 	89
Accuracy: 0.0268359 	90
Accuracy: 0.0270657 	91
Accuracy: 0.0272971 	92
Accuracy: 0.0275304 	93
Accuracy: 0.0277655 	94
Accuracy: 0.0280024 	95
Accuracy: 0.0282411 	96
Accuracy: 0.0284819 	97
Accuracy: 0.0287247 	98
Accuracy: 0.0289697 	99
[(0.065011717, 50), (0.027317617, 45), (0.017220482, 41), (0.013026549, 67), (0.014426506, 41), (0.028969711, 99)]
Interp bead indices: 
[1, 2, 3, 4, 5, 6]
[1, 2]
Accuracy: 0.0125008
Final bead error: 0.0125008
[True, True, True, True, True, True, True, True, True]
Accuracy: 0.0127198 	0
Accuracy: 0.0127155 	1
Accuracy: 0.0127113 	2
Accuracy: 0.0127073 	3
Accuracy: 0.0127033 	4
Accuracy: 0.0126994 	5
Accuracy: 0.0126955 	6
Accuracy: 0.0126917 	7
Accuracy: 0.0126878 	8
Accuracy: 0.012684 	9
Accuracy: 0.01268 	10
Accuracy: 0.012676 	11
Accuracy: 0.0126719 	12
Accuracy: 0.0126676 	13
Accuracy: 0.0126632 	14
Accuracy: 0.0126587 	15
Accuracy: 0.012654 	16
Accuracy: 0.012649 	17
Accuracy: 0.0126439 	18
Accuracy: 0.0126385 	19
Accuracy: 0.0126328 	20
Accuracy: 0.0126269 	21
Accuracy: 0.0126207 	22
Accuracy: 0.0126141 	23
Accuracy: 0.0126073 	24
Accuracy: 0.0126001 	25
Accuracy: 0.0125925 	26
Accuracy: 0.0125846 	27
Accuracy: 0.0125762 	28
Accuracy: 0.0125675 	29
Accuracy: 0.0125583 	30
Accuracy: 0.0125487 	31
Accuracy: 0.0125387 	32
Accuracy: 0.0125282 	33
Accuracy: 0.0125172 	34
Accuracy: 0.0125058 	35
Accuracy: 0.0124938 	36
Accuracy: 0.0124814 	37
Accuracy: 0.0124684 	38
Accuracy: 0.0124549 	39
Accuracy: 0.0124409 	40
Accuracy: 0.0124263 	41
Accuracy: 0.0124111 	42
Accuracy: 0.0123954 	43
Accuracy: 0.0123791 	44
Accuracy: 0.0123623 	45
Accuracy: 0.0123448 	46
Accuracy: 0.0123268 	47
Accuracy: 0.0123081 	48
Accuracy: 0.0122889 	49
Accuracy: 0.012269 	50
Accuracy: 0.0122485 	51
Accuracy: 0.0122274 	52
Accuracy: 0.0122057 	53
Accuracy: 0.0121833 	54
Accuracy: 0.0121603 	55
Accuracy: 0.0121366 	56
Accuracy: 0.0121123 	57
Accuracy: 0.0120874 	58
Accuracy: 0.0120618 	59
Accuracy: 0.0120355 	60
Accuracy: 0.0120086 	61
Accuracy: 0.0119811 	62
Accuracy: 0.0119528 	63
Accuracy: 0.011924 	64
Accuracy: 0.0118944 	65
Accuracy: 0.0118643 	66
Accuracy: 0.0118334 	67
Accuracy: 0.0118032 	68
Accuracy: 0.011777 	69
Accuracy: 0.0117536 	70
Accuracy: 0.011732 	71
Accuracy: 0.0117124 	72
Accuracy: 0.0116962 	73
Accuracy: 0.0116851 	74
Accuracy: 0.0116787 	75
Accuracy: 0.0116761 	76
Accuracy: 0.0116774 	77
Accuracy: 0.0116827 	78
Accuracy: 0.0116917 	79
Accuracy: 0.0117049 	80
Accuracy: 0.0117231 	81
Accuracy: 0.0117465 	82
Accuracy: 0.011774 	83
Accuracy: 0.0118059 	84
Accuracy: 0.011842 	85
Accuracy: 0.0118823 	86
Accuracy: 0.0119288 	87
Accuracy: 0.0119804 	88
Accuracy: 0.012037 	89
Accuracy: 0.0121002 	90
Accuracy: 0.01217 	91
Accuracy: 0.0122464 	92
Accuracy: 0.0123285 	93
Accuracy: 0.0124175 	94
Accuracy: 0.0125127 	95
Accuracy: 0.0126134 	96
Accuracy: 0.0127196 	97
Accuracy: 0.0128334 	98
Accuracy: 0.012954 	99
Accuracy: 0.0126421 	0
Accuracy: 0.0129922 	1
Accuracy: 0.0133729 	2
Accuracy: 0.0137797 	3
Accuracy: 0.0142096 	4
Accuracy: 0.0146592 	5
Accuracy: 0.0151245 	6
Accuracy: 0.0156021 	7
Accuracy: 0.0160885 	8
Accuracy: 0.0165784 	9
Accuracy: 0.017066 	10
Accuracy: 0.0175482 	11
Accuracy: 0.0180206 	12
Accuracy: 0.0184742 	13
Accuracy: 0.0189079 	14
Accuracy: 0.0193102 	15
Accuracy: 0.0196717 	16
Accuracy: 0.0199945 	17
Accuracy: 0.0202728 	18
Accuracy: 0.0204863 	19
Accuracy: 0.0206434 	20
Accuracy: 0.0207277 	21
Accuracy: 0.020746 	22
Accuracy: 0.0207598 	23
Accuracy: 0.0207708 	24
Accuracy: 0.0207788 	25
Accuracy: 0.0207839 	26
Accuracy: 0.0207859 	27
Accuracy: 0.0207848 	28
Accuracy: 0.0207805 	29
Accuracy: 0.0207731 	30
Accuracy: 0.0207623 	31
Accuracy: 0.0207483 	32
Accuracy: 0.020731 	33
Accuracy: 0.0207103 	34
Accuracy: 0.0206862 	35
Accuracy: 0.0206587 	36
Accuracy: 0.0206278 	37
Accuracy: 0.0205935 	38
Accuracy: 0.0205557 	39
Accuracy: 0.0205145 	40
Accuracy: 0.0204698 	41
Accuracy: 0.0204217 	42
Accuracy: 0.0203702 	43
Accuracy: 0.0203153 	44
Accuracy: 0.0202569 	45
Accuracy: 0.0201952 	46
Accuracy: 0.0201301 	47
Accuracy: 0.0200618 	48
Accuracy: 0.0199901 	49
Accuracy: 0.0199152 	50
Accuracy: 0.0198372 	51
Accuracy: 0.0197559 	52
Accuracy: 0.0196716 	53
Accuracy: 0.0195842 	54
Accuracy: 0.0194939 	55
Accuracy: 0.0194006 	56
Accuracy: 0.0193045 	57
Accuracy: 0.0192057 	58
Accuracy: 0.0191041 	59
Accuracy: 0.0189999 	60
Accuracy: 0.0188932 	61
Accuracy: 0.0187841 	62
Accuracy: 0.0186726 	63
Accuracy: 0.0185589 	64
Accuracy: 0.018443 	65
Accuracy: 0.0183251 	66
Accuracy: 0.0182052 	67
Accuracy: 0.0180835 	68
Accuracy: 0.0179602 	69
Accuracy: 0.0178352 	70
Accuracy: 0.0177088 	71
Accuracy: 0.017581 	72
Accuracy: 0.0174521 	73
Accuracy: 0.0173221 	74
Accuracy: 0.0171912 	75
Accuracy: 0.0170595 	76
Accuracy: 0.0169272 	77
Accuracy: 0.0167944 	78
Accuracy: 0.0166612 	79
Accuracy: 0.0165279 	80
Accuracy: 0.0163946 	81
Accuracy: 0.0162615 	82
Accuracy: 0.0161287 	83
Accuracy: 0.0159964 	84
Accuracy: 0.0158648 	85
Accuracy: 0.0157341 	86
Accuracy: 0.0156044 	87
Accuracy: 0.0154759 	88
Accuracy: 0.0153489 	89
Accuracy: 0.0152235 	90
Accuracy: 0.0150999 	91
Accuracy: 0.0149783 	92
Accuracy: 0.014859 	93
Accuracy: 0.0147421 	94
Accuracy: 0.0146278 	95
Accuracy: 0.0145165 	96
Accuracy: 0.0144082 	97
Accuracy: 0.0143032 	98
Accuracy: 0.0142017 	99
[(0.012954038, 99), (0.020785892, 27)]
Interp bead indices: 
[1, 2]
[0, 1]
Accuracy: 0.0482437
Accuracy: 0.0367408
Accuracy: 0.0286126
Accuracy: 0.022156
Accuracy: 0.0169639
Accuracy: 0.0166682
Final bead error: 0.0166682
[True, True, True]
Accuracy: 0.0280968 	0
Accuracy: 0.028844 	1
Accuracy: 0.0297405 	2
Accuracy: 0.0307732 	3
Accuracy: 0.0319293 	4
Accuracy: 0.0331967 	5
Accuracy: 0.0345633 	6
Accuracy: 0.0360175 	7
Accuracy: 0.0375483 	8
Accuracy: 0.0391448 	9
Accuracy: 0.0407966 	10
Accuracy: 0.0424936 	11
Accuracy: 0.0442261 	12
Accuracy: 0.0459848 	13
Accuracy: 0.0477417 	14
Accuracy: 0.0494275 	15
Accuracy: 0.0510577 	16
Accuracy: 0.0526191 	17
Accuracy: 0.0541144 	18
Accuracy: 0.0555378 	19
Accuracy: 0.0568486 	20
Accuracy: 0.0580986 	21
Accuracy: 0.0593072 	22
Accuracy: 0.0604586 	23
Accuracy: 0.0615637 	24
Accuracy: 0.0626253 	25
Accuracy: 0.0636206 	26
Accuracy: 0.0645503 	27
Accuracy: 0.0654014 	28
Accuracy: 0.0661952 	29
Accuracy: 0.0669411 	30
Accuracy: 0.0676306 	31
Accuracy: 0.0682497 	32
Accuracy: 0.0687928 	33
Accuracy: 0.0692573 	34
Accuracy: 0.0696419 	35
Accuracy: 0.0699493 	36
Accuracy: 0.0701771 	37
Accuracy: 0.0703285 	38
Accuracy: 0.0704028 	39
Accuracy: 0.0704033 	40
Accuracy: 0.0703326 	41
Accuracy: 0.0701936 	42
Accuracy: 0.0699869 	43
Accuracy: 0.0697108 	44
Accuracy: 0.0693655 	45
Accuracy: 0.0689538 	46
Accuracy: 0.0684775 	47
Accuracy: 0.0679401 	48
Accuracy: 0.0673422 	49
Accuracy: 0.0666856 	50
Accuracy: 0.0659699 	51
Accuracy: 0.0652114 	52
Accuracy: 0.0644003 	53
Accuracy: 0.0635561 	54
Accuracy: 0.0626678 	55
Accuracy: 0.0617315 	56
Accuracy: 0.0607592 	57
Accuracy: 0.0597494 	58
Accuracy: 0.0587049 	59
Accuracy: 0.0576398 	60
Accuracy: 0.0565717 	61
Accuracy: 0.0554991 	62
Accuracy: 0.0544385 	63
Accuracy: 0.0534032 	64
Accuracy: 0.0524098 	65
Accuracy: 0.0514488 	66
Accuracy: 0.050545 	67
Accuracy: 0.0497086 	68
Accuracy: 0.0489416 	69
Accuracy: 0.0482551 	70
Accuracy: 0.0476637 	71
Accuracy: 0.0471913 	72
Accuracy: 0.0468549 	73
Accuracy: 0.0466837 	74
Accuracy: 0.0466948 	75
Accuracy: 0.0469092 	76
Accuracy: 0.0473446 	77
Accuracy: 0.0480156 	78
Accuracy: 0.0489498 	79
Accuracy: 0.0501743 	80
Accuracy: 0.0512694 	81
Accuracy: 0.0510074 	82
Accuracy: 0.0498297 	83
Accuracy: 0.0480183 	84
Accuracy: 0.0453246 	85
Accuracy: 0.0419889 	86
Accuracy: 0.0383431 	87
Accuracy: 0.0345218 	88
Accuracy: 0.030756 	89
Accuracy: 0.0272045 	90
Accuracy: 0.0239605 	91
Accuracy: 0.0211293 	92
Accuracy: 0.0187845 	93
Accuracy: 0.0170152 	94
Accuracy: 0.0157674 	95
Accuracy: 0.0148696 	96
Accuracy: 0.014271 	97
Accuracy: 0.0139642 	98
Accuracy: 0.0139389 	99
Accuracy: 0.0158617 	0
Accuracy: 0.0181162 	1
Accuracy: 0.0227436 	2
Accuracy: 0.0297573 	3
Accuracy: 0.0391656 	4
Accuracy: 0.0509707 	5
Accuracy: 0.0651694 	6
Accuracy: 0.0817544 	7
Accuracy: 0.100714 	8
Accuracy: 0.12203 	9
Accuracy: 0.145684 	10
Accuracy: 0.171645 	11
Accuracy: 0.199879 	12
Accuracy: 0.230352 	13
Accuracy: 0.263025 	14
Accuracy: 0.297848 	15
Accuracy: 0.334774 	16
Accuracy: 0.373746 	17
Accuracy: 0.414705 	18
Accuracy: 0.457586 	19
Accuracy: 0.502321 	20
Accuracy: 0.548759 	21
Accuracy: 0.596278 	22
Accuracy: 0.644753 	23
Accuracy: 0.694117 	24
Accuracy: 0.744031 	25
Accuracy: 0.794272 	26
Accuracy: 0.844718 	27
Accuracy: 0.895574 	28
Accuracy: 0.946779 	29
Accuracy: 0.99819 	30
Accuracy: 1.04966 	31
Accuracy: 1.10104 	32
Accuracy: 1.15218 	33
Accuracy: 1.20294 	34
Accuracy: 1.25314 	35
Accuracy: 1.30266 	36
Accuracy: 1.35132 	37
Accuracy: 1.39898 	38
Accuracy: 1.44548 	39
Accuracy: 1.49067 	40
Accuracy: 1.5344 	41
Accuracy: 1.57654 	42
Accuracy: 1.61691 	43
Accuracy: 1.65539 	44
Accuracy: 1.69182 	45
Accuracy: 1.72606 	46
Accuracy: 1.75799 	47
Accuracy: 1.78746 	48
Accuracy: 1.81436 	49
Accuracy: 1.83856 	50
Accuracy: 1.85994 	51
Accuracy: 1.8784 	52
Accuracy: 1.89384 	53
Accuracy: 1.90615 	54
Accuracy: 1.91525 	55
Accuracy: 1.92106 	56
Accuracy: 1.9235 	57
Accuracy: 1.92251 	58
Accuracy: 1.91804 	59
Accuracy: 1.91004 	60
Accuracy: 1.89848 	61
Accuracy: 1.88335 	62
Accuracy: 1.86461 	63
Accuracy: 1.84229 	64
Accuracy: 1.81638 	65
Accuracy: 1.78691 	66
Accuracy: 1.75392 	67
Accuracy: 1.71747 	68
Accuracy: 1.67762 	69
Accuracy: 1.63446 	70
Accuracy: 1.58809 	71
Accuracy: 1.53862 	72
Accuracy: 1.48619 	73
Accuracy: 1.43095 	74
Accuracy: 1.37308 	75
Accuracy: 1.31276 	76
Accuracy: 1.25021 	77
Accuracy: 1.18567 	78
Accuracy: 1.11938 	79
Accuracy: 1.05163 	80
Accuracy: 0.982722 	81
Accuracy: 0.913162 	82
Accuracy: 0.843564 	83
Accuracy: 0.774233 	84
Accuracy: 0.705571 	85
Accuracy: 0.637864 	86
Accuracy: 0.57145 	87
Accuracy: 0.506788 	88
Accuracy: 0.444232 	89
Accuracy: 0.384188 	90
Accuracy: 0.327083 	91
Accuracy: 0.273397 	92
Accuracy: 0.223613 	93
Accuracy: 0.178282 	94
Accuracy: 0.137961 	95
Accuracy: 0.103171 	96
Accuracy: 0.0745247 	97
Accuracy: 0.0526102 	98
Accuracy: 0.0380634 	99
[(0.070403285, 40), (1.9234983, 57)]
Interp bead indices: 
[0, 1]
[0, 1, 2, 3]
Accuracy: 0.0132413
Final bead error: 0.0132413
Accuracy: 0.0330774
Accuracy: 0.0244054
Accuracy: 0.0177034
Accuracy: 0.0154029
Final bead error: 0.0154029
[True, True, True, True, True]
Accuracy: 0.0311707 	0
Accuracy: 0.0311778 	1
Accuracy: 0.0311871 	2
Accuracy: 0.0311986 	3
Accuracy: 0.0312121 	4
Accuracy: 0.0312276 	5
Accuracy: 0.0312451 	6
Accuracy: 0.0312643 	7
Accuracy: 0.0312853 	8
Accuracy: 0.031308 	9
Accuracy: 0.0313323 	10
Accuracy: 0.0313582 	11
Accuracy: 0.0313855 	12
Accuracy: 0.0314143 	13
Accuracy: 0.0314445 	14
Accuracy: 0.0314761 	15
Accuracy: 0.0315089 	16
Accuracy: 0.0315429 	17
Accuracy: 0.0315782 	18
Accuracy: 0.0316146 	19
Accuracy: 0.0316522 	20
Accuracy: 0.0316909 	21
Accuracy: 0.0317306 	22
Accuracy: 0.0317713 	23
Accuracy: 0.0318131 	24
Accuracy: 0.0318559 	25
Accuracy: 0.0318996 	26
Accuracy: 0.0319443 	27
Accuracy: 0.0319899 	28
Accuracy: 0.0320364 	29
Accuracy: 0.0320822 	30
Accuracy: 0.0321021 	31
Accuracy: 0.0321013 	32
Accuracy: 0.0320907 	33
Accuracy: 0.0320519 	34
Accuracy: 0.0319889 	35
Accuracy: 0.0318991 	36
Accuracy: 0.0317988 	37
Accuracy: 0.0316749 	38
Accuracy: 0.0315202 	39
Accuracy: 0.031333 	40
Accuracy: 0.0311223 	41
Accuracy: 0.0308994 	42
Accuracy: 0.0306621 	43
Accuracy: 0.0304105 	44
Accuracy: 0.0301515 	45
Accuracy: 0.0298776 	46
Accuracy: 0.0295997 	47
Accuracy: 0.0293279 	48
Accuracy: 0.0290473 	49
Accuracy: 0.0287586 	50
Accuracy: 0.0284447 	51
Accuracy: 0.0281214 	52
Accuracy: 0.0277964 	53
Accuracy: 0.0274594 	54
Accuracy: 0.0271297 	55
Accuracy: 0.0268031 	56
Accuracy: 0.0264776 	57
Accuracy: 0.0261419 	58
Accuracy: 0.0257945 	59
Accuracy: 0.0254488 	60
Accuracy: 0.0250987 	61
Accuracy: 0.0247413 	62
Accuracy: 0.0243863 	63
Accuracy: 0.0240326 	64
Accuracy: 0.0236735 	65
Accuracy: 0.0233122 	66
Accuracy: 0.0229482 	67
Accuracy: 0.022592 	68
Accuracy: 0.0222356 	69
Accuracy: 0.0218831 	70
Accuracy: 0.0215375 	71
Accuracy: 0.0211968 	72
Accuracy: 0.0208596 	73
Accuracy: 0.0205153 	74
Accuracy: 0.0201697 	75
Accuracy: 0.0198176 	76
Accuracy: 0.0194726 	77
Accuracy: 0.0191343 	78
Accuracy: 0.0188036 	79
Accuracy: 0.0184764 	80
Accuracy: 0.0181533 	81
Accuracy: 0.0178377 	82
Accuracy: 0.01753 	83
Accuracy: 0.0172276 	84
Accuracy: 0.0169294 	85
Accuracy: 0.0166355 	86
Accuracy: 0.016345 	87
Accuracy: 0.0160643 	88
Accuracy: 0.0157897 	89
Accuracy: 0.0155222 	90
Accuracy: 0.0152568 	91
Accuracy: 0.0149935 	92
Accuracy: 0.0147319 	93
Accuracy: 0.0144762 	94
Accuracy: 0.0142232 	95
Accuracy: 0.0139735 	96
Accuracy: 0.0137266 	97
Accuracy: 0.0134872 	98
Accuracy: 0.0132553 	99
Accuracy: 0.0129494 	0
Accuracy: 0.0130881 	1
Accuracy: 0.0132627 	2
Accuracy: 0.0134724 	3
Accuracy: 0.0137165 	4
Accuracy: 0.0139932 	5
Accuracy: 0.014303 	6
Accuracy: 0.0146403 	7
Accuracy: 0.0150014 	8
Accuracy: 0.0153865 	9
Accuracy: 0.0157958 	10
Accuracy: 0.0162243 	11
Accuracy: 0.0166706 	12
Accuracy: 0.0171316 	13
Accuracy: 0.0176056 	14
Accuracy: 0.0180918 	15
Accuracy: 0.0185895 	16
Accuracy: 0.0190959 	17
Accuracy: 0.0196096 	18
Accuracy: 0.0201295 	19
Accuracy: 0.020654 	20
Accuracy: 0.021182 	21
Accuracy: 0.0217122 	22
Accuracy: 0.0222438 	23
Accuracy: 0.0227749 	24
Accuracy: 0.0233021 	25
Accuracy: 0.0238242 	26
Accuracy: 0.0243415 	27
Accuracy: 0.0248554 	28
Accuracy: 0.0253671 	29
Accuracy: 0.025876 	30
Accuracy: 0.0263817 	31
Accuracy: 0.0268818 	32
Accuracy: 0.0273761 	33
Accuracy: 0.0278634 	34
Accuracy: 0.028345 	35
Accuracy: 0.0288189 	36
Accuracy: 0.0292859 	37
Accuracy: 0.0297532 	38
Accuracy: 0.0302198 	39
Accuracy: 0.0306843 	40
Accuracy: 0.0311493 	41
Accuracy: 0.0316153 	42
Accuracy: 0.0320786 	43
Accuracy: 0.0325427 	44
Accuracy: 0.0330136 	45
Accuracy: 0.0335026 	46
Accuracy: 0.034003 	47
Accuracy: 0.0345161 	48
Accuracy: 0.0350443 	49
Accuracy: 0.0355868 	50
Accuracy: 0.0361488 	51
Accuracy: 0.0367348 	52
Accuracy: 0.0373446 	53
Accuracy: 0.0379858 	54
Accuracy: 0.0386651 	55
Accuracy: 0.0393777 	56
Accuracy: 0.0401334 	57
Accuracy: 0.0409395 	58
Accuracy: 0.0417962 	59
Accuracy: 0.0427082 	60
Accuracy: 0.0436788 	61
Accuracy: 0.0447134 	62
Accuracy: 0.0458185 	63
Accuracy: 0.0469955 	64
Accuracy: 0.048247 	65
Accuracy: 0.0495803 	66
Accuracy: 0.050953 	67
Accuracy: 0.0519837 	68
Accuracy: 0.0524297 	69
Accuracy: 0.0523659 	70
Accuracy: 0.0518886 	71
Accuracy: 0.050988 	72
Accuracy: 0.0497108 	73
Accuracy: 0.0482378 	74
Accuracy: 0.04656 	75
Accuracy: 0.0446857 	76
Accuracy: 0.042639 	77
Accuracy: 0.0404938 	78
Accuracy: 0.0382701 	79
Accuracy: 0.0360083 	80
Accuracy: 0.0337577 	81
Accuracy: 0.0315517 	82
Accuracy: 0.029413 	83
Accuracy: 0.0273745 	84
Accuracy: 0.0254532 	85
Accuracy: 0.0236716 	86
Accuracy: 0.0220473 	87
Accuracy: 0.0205902 	88
Accuracy: 0.019318 	89
Accuracy: 0.0182454 	90
Accuracy: 0.017364 	91
Accuracy: 0.016624 	92
Accuracy: 0.0159955 	93
Accuracy: 0.0154772 	94
Accuracy: 0.0150685 	95
Accuracy: 0.0147683 	96
Accuracy: 0.014575 	97
Accuracy: 0.0144853 	98
Accuracy: 0.0144974 	99
Accuracy: 0.015518 	0
Accuracy: 0.0160568 	1
Accuracy: 0.0169062 	2
Accuracy: 0.0180724 	3
Accuracy: 0.0195599 	4
Accuracy: 0.0213714 	5
Accuracy: 0.0235104 	6
Accuracy: 0.0259794 	7
Accuracy: 0.0287816 	8
Accuracy: 0.0319175 	9
Accuracy: 0.0353872 	10
Accuracy: 0.0391932 	11
Accuracy: 0.043337 	12
Accuracy: 0.047818 	13
Accuracy: 0.0526368 	14
Accuracy: 0.0577935 	15
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Accuracy: 0.100219 	22
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Accuracy: 0.132871 	27
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Accuracy: 0.14627 	29
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Accuracy: 0.159686 	31
Accuracy: 0.166357 	32
Accuracy: 0.172977 	33
Accuracy: 0.179528 	34
Accuracy: 0.185991 	35
Accuracy: 0.192348 	36
Accuracy: 0.198577 	37
Accuracy: 0.204662 	38
Accuracy: 0.210582 	39
Accuracy: 0.216319 	40
Accuracy: 0.221854 	41
Accuracy: 0.227169 	42
Accuracy: 0.232245 	43
Accuracy: 0.237065 	44
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Accuracy: 0.256722 	49
Accuracy: 0.259652 	50
Accuracy: 0.262214 	51
Accuracy: 0.264394 	52
Accuracy: 0.26618 	53
Accuracy: 0.26756 	54
Accuracy: 0.268524 	55
Accuracy: 0.269062 	56
Accuracy: 0.269163 	57
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Accuracy: 0.262918 	62
Accuracy: 0.260289 	63
Accuracy: 0.257198 	64
Accuracy: 0.253646 	65
Accuracy: 0.249638 	66
Accuracy: 0.245179 	67
Accuracy: 0.240276 	68
Accuracy: 0.234937 	69
Accuracy: 0.229174 	70
Accuracy: 0.222998 	71
Accuracy: 0.216426 	72
Accuracy: 0.209474 	73
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Accuracy: 0.186552 	76
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Accuracy: 0.115338 	84
Accuracy: 0.106084 	85
Accuracy: 0.0969099 	86
Accuracy: 0.0878724 	87
Accuracy: 0.0790311 	88
Accuracy: 0.0704497 	89
Accuracy: 0.0621954 	90
Accuracy: 0.0543386 	91
Accuracy: 0.0469546 	92
Accuracy: 0.0401219 	93
Accuracy: 0.0339232 	94
Accuracy: 0.0284455 	95
Accuracy: 0.0237798 	96
Accuracy: 0.0200216 	97
Accuracy: 0.0172708 	98
Accuracy: 0.0156318 	99
Accuracy: 0.0152345 	0
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Accuracy: 0.0177345 	3
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Accuracy: 0.0202547 	5
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Accuracy: 0.0289396 	10
Accuracy: 0.0310059 	11
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Accuracy: 0.057568 	22
Accuracy: 0.0601143 	23
Accuracy: 0.0626494 	24
Accuracy: 0.0651662 	25
Accuracy: 0.0676577 	26
Accuracy: 0.0701177 	27
Accuracy: 0.0725396 	28
Accuracy: 0.0749174 	29
Accuracy: 0.0772449 	30
Accuracy: 0.0795167 	31
Accuracy: 0.0817271 	32
Accuracy: 0.0838709 	33
Accuracy: 0.0859432 	34
Accuracy: 0.087939 	35
Accuracy: 0.0898537 	36
Accuracy: 0.0916832 	37
Accuracy: 0.0934233 	38
Accuracy: 0.09507 	39
Accuracy: 0.09662 	40
Accuracy: 0.0980698 	41
Accuracy: 0.0994162 	42
Accuracy: 0.100657 	43
Accuracy: 0.101788 	44
Accuracy: 0.102809 	45
Accuracy: 0.103717 	46
Accuracy: 0.10451 	47
Accuracy: 0.105186 	48
Accuracy: 0.105745 	49
Accuracy: 0.106186 	50
Accuracy: 0.106507 	51
Accuracy: 0.106709 	52
Accuracy: 0.106791 	53
Accuracy: 0.106754 	54
Accuracy: 0.106598 	55
Accuracy: 0.106323 	56
Accuracy: 0.105928 	57
Accuracy: 0.105415 	58
Accuracy: 0.104785 	59
Accuracy: 0.104038 	60
Accuracy: 0.103178 	61
Accuracy: 0.102207 	62
Accuracy: 0.101125 	63
Accuracy: 0.0999368 	64
Accuracy: 0.0986445 	65
Accuracy: 0.0972513 	66
Accuracy: 0.0957605 	67
Accuracy: 0.0941755 	68
Accuracy: 0.0925009 	69
Accuracy: 0.0907408 	70
Accuracy: 0.0888995 	71
Accuracy: 0.0869821 	72
Accuracy: 0.0849939 	73
Accuracy: 0.0829402 	74
Accuracy: 0.0808267 	75
Accuracy: 0.0786596 	76
Accuracy: 0.0764449 	77
Accuracy: 0.0741884 	78
Accuracy: 0.0718974 	79
Accuracy: 0.0695797 	80
Accuracy: 0.0672434 	81
Accuracy: 0.0648933 	82
Accuracy: 0.0625362 	83
Accuracy: 0.0601828 	84
Accuracy: 0.0578417 	85
Accuracy: 0.0555208 	86
Accuracy: 0.0532268 	87
Accuracy: 0.0509688 	88
Accuracy: 0.0487554 	89
Accuracy: 0.046594 	90
Accuracy: 0.0444992 	91
Accuracy: 0.042491 	92
Accuracy: 0.0405847 	93
Accuracy: 0.0387785 	94
Accuracy: 0.0370847 	95
Accuracy: 0.0355141 	96
Accuracy: 0.0340909 	97
Accuracy: 0.0328226 	98
Accuracy: 0.0318411 	99
[(0.032102142, 31), (0.052429724, 69), (0.26916346, 57), (0.10679145, 53)]
Interp bead indices: 
[0, 1, 2, 3]
[1, 2, 3, 4, 5, 6]
Accuracy: 0.0122218
Final bead error: 0.0122218
Accuracy: 0.0142399
Final bead error: 0.0142399
Accuracy: 0.0332163
Accuracy: 0.0167764
Accuracy: 0.0148501
Final bead error: 0.0148501
[True, True, True, True, True, True, True, True]
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Accuracy: 0.0137406 	2
Accuracy: 0.013805 	3
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Accuracy: 0.0140625 	6
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Accuracy: 0.0142853 	8
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Accuracy: 0.0151516 	14
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Accuracy: 0.0231717 	92
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Accuracy: 0.0108346 	24
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Accuracy: 0.0108387 	46
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Accuracy: 0.0159829 	7
Accuracy: 0.0162605 	8
Accuracy: 0.0165565 	9
Accuracy: 0.0168711 	10
Accuracy: 0.0172047 	11
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Accuracy: 0.0179302 	13
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Accuracy: 0.0200919 	18
Accuracy: 0.0205868 	19
Accuracy: 0.0211035 	20
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Accuracy: 0.0222038 	22
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Accuracy: 0.0290428 	65
Accuracy: 0.0287305 	66
Accuracy: 0.0283948 	67
Accuracy: 0.0280364 	68
Accuracy: 0.0276559 	69
Accuracy: 0.0272539 	70
Accuracy: 0.0268315 	71
Accuracy: 0.0263894 	72
Accuracy: 0.0259287 	73
Accuracy: 0.0254505 	74
Accuracy: 0.0249562 	75
Accuracy: 0.024447 	76
Accuracy: 0.0239244 	77
Accuracy: 0.0233901 	78
Accuracy: 0.0228456 	79
Accuracy: 0.022293 	80
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Accuracy: 0.0194806 	85
Accuracy: 0.0189249 	86
Accuracy: 0.0183779 	87
Accuracy: 0.0178423 	88
Accuracy: 0.0173215 	89
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Accuracy: 0.0163372 	91
Accuracy: 0.015881 	92
Accuracy: 0.0154537 	93
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Accuracy: 0.0138978 	98
Accuracy: 0.0137346 	99
Accuracy: 0.0141445 	0
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Accuracy: 0.0141433 	2
Accuracy: 0.0142194 	3
Accuracy: 0.0143431 	4
Accuracy: 0.0145119 	5
Accuracy: 0.0147232 	6
Accuracy: 0.0149744 	7
Accuracy: 0.0152631 	8
Accuracy: 0.0155867 	9
Accuracy: 0.0159428 	10
Accuracy: 0.016329 	11
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Accuracy: 0.0171819 	13
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Accuracy: 0.0191447 	17
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Accuracy: 0.0202184 	19
Accuracy: 0.0207707 	20
Accuracy: 0.0213306 	21
Accuracy: 0.0218958 	22
Accuracy: 0.0224644 	23
Accuracy: 0.0230345 	24
Accuracy: 0.0236042 	25
Accuracy: 0.0241714 	26
Accuracy: 0.0247345 	27
Accuracy: 0.0252915 	28
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Accuracy: 0.0269093 	31
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Accuracy: 0.0279273 	33
Accuracy: 0.0284134 	34
Accuracy: 0.0288825 	35
Accuracy: 0.0293331 	36
Accuracy: 0.0297639 	37
Accuracy: 0.0301736 	38
Accuracy: 0.0305612 	39
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Accuracy: 0.0325644 	46
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Accuracy: 0.0328834 	48
Accuracy: 0.0329974 	49
Accuracy: 0.0330806 	50
Accuracy: 0.0331324 	51
Accuracy: 0.0331527 	52
Accuracy: 0.0331412 	53
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Accuracy: 0.0330227 	55
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Accuracy: 0.0326064 	58
Accuracy: 0.0324049 	59
Accuracy: 0.0321726 	60
Accuracy: 0.03191 	61
Accuracy: 0.0316177 	62
Accuracy: 0.0312963 	63
Accuracy: 0.0309466 	64
Accuracy: 0.0305695 	65
Accuracy: 0.0301659 	66
Accuracy: 0.029737 	67
Accuracy: 0.0292837 	68
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Accuracy: 0.0283098 	70
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Accuracy: 0.0272554 	72
Accuracy: 0.026702 	73
Accuracy: 0.0261336 	74
Accuracy: 0.025552 	75
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Accuracy: 0.0243577 	77
Accuracy: 0.0237492 	78
Accuracy: 0.0231363 	79
Accuracy: 0.0225216 	80
Accuracy: 0.0219077 	81
Accuracy: 0.0212972 	82
Accuracy: 0.020693 	83
Accuracy: 0.0200981 	84
Accuracy: 0.0195156 	85
Accuracy: 0.0189486 	86
Accuracy: 0.0184007 	87
Accuracy: 0.0178752 	88
Accuracy: 0.0173759 	89
Accuracy: 0.0169064 	90
Accuracy: 0.0164706 	91
Accuracy: 0.0160727 	92
Accuracy: 0.0157167 	93
Accuracy: 0.0154069 	94
Accuracy: 0.0151479 	95
Accuracy: 0.0149441 	96
Accuracy: 0.0148003 	97
Accuracy: 0.0147214 	98
Accuracy: 0.0147125 	99
Accuracy: 0.0144933 	0
Accuracy: 0.0147898 	1
Accuracy: 0.0151562 	2
Accuracy: 0.0155882 	3
Accuracy: 0.0160816 	4
Accuracy: 0.0166323 	5
Accuracy: 0.0172362 	6
Accuracy: 0.0178894 	7
Accuracy: 0.018588 	8
Accuracy: 0.0193281 	9
Accuracy: 0.020106 	10
Accuracy: 0.0209179 	11
Accuracy: 0.0217602 	12
Accuracy: 0.0226294 	13
Accuracy: 0.0235219 	14
Accuracy: 0.0244344 	15
Accuracy: 0.0253634 	16
Accuracy: 0.0263058 	17
Accuracy: 0.0272584 	18
Accuracy: 0.0282179 	19
Accuracy: 0.0291815 	20
Accuracy: 0.0301461 	21
Accuracy: 0.0311088 	22
Accuracy: 0.0320669 	23
Accuracy: 0.0330176 	24
Accuracy: 0.0339582 	25
Accuracy: 0.0348863 	26
Accuracy: 0.0357993 	27
Accuracy: 0.0366949 	28
Accuracy: 0.0375706 	29
Accuracy: 0.0384244 	30
Accuracy: 0.0392541 	31
Accuracy: 0.0400575 	32
Accuracy: 0.0408328 	33
Accuracy: 0.0415782 	34
Accuracy: 0.0422916 	35
Accuracy: 0.0429715 	36
Accuracy: 0.0436164 	37
Accuracy: 0.0442245 	38
Accuracy: 0.0447946 	39
Accuracy: 0.0453254 	40
Accuracy: 0.0458155 	41
Accuracy: 0.0462637 	42
Accuracy: 0.0466692 	43
Accuracy: 0.047031 	44
Accuracy: 0.0473481 	45
Accuracy: 0.0476198 	46
Accuracy: 0.0478455 	47
Accuracy: 0.0480247 	48
Accuracy: 0.0481568 	49
Accuracy: 0.0482415 	50
Accuracy: 0.0482785 	51
Accuracy: 0.0482678 	52
Accuracy: 0.0482094 	53
Accuracy: 0.048103 	54
Accuracy: 0.0479492 	55
Accuracy: 0.047748 	56
Accuracy: 0.0474999 	57
Accuracy: 0.0472054 	58
Accuracy: 0.046865 	59
Accuracy: 0.0464795 	60
Accuracy: 0.0460497 	61
Accuracy: 0.0455766 	62
Accuracy: 0.0450611 	63
Accuracy: 0.0445045 	64
Accuracy: 0.0439081 	65
Accuracy: 0.0432732 	66
Accuracy: 0.0426013 	67
Accuracy: 0.041894 	68
Accuracy: 0.0411532 	69
Accuracy: 0.0403806 	70
Accuracy: 0.0395774 	71
Accuracy: 0.038745 	72
Accuracy: 0.0378855 	73
Accuracy: 0.037001 	74
Accuracy: 0.0360936 	75
Accuracy: 0.0351655 	76
Accuracy: 0.034219 	77
Accuracy: 0.0332562 	78
Accuracy: 0.0322805 	79
Accuracy: 0.0312943 	80
Accuracy: 0.0302993 	81
Accuracy: 0.029298 	82
Accuracy: 0.0282934 	83
Accuracy: 0.0272891 	84
Accuracy: 0.0262877 	85
Accuracy: 0.025292 	86
Accuracy: 0.0243055 	87
Accuracy: 0.0233314 	88
Accuracy: 0.0223733 	89
Accuracy: 0.021435 	90
Accuracy: 0.0205207 	91
Accuracy: 0.0196346 	92
Accuracy: 0.0187808 	93
Accuracy: 0.0179646 	94
Accuracy: 0.0171945 	95
Accuracy: 0.0164768 	96
Accuracy: 0.0158208 	97
Accuracy: 0.0152392 	98
Accuracy: 0.0147391 	99
Accuracy: 0.014176 	0
Accuracy: 0.0142865 	1
Accuracy: 0.0143977 	2
Accuracy: 0.0145096 	3
Accuracy: 0.0146222 	4
Accuracy: 0.0147356 	5
Accuracy: 0.0148496 	6
Accuracy: 0.0149644 	7
Accuracy: 0.0150799 	8
Accuracy: 0.0151961 	9
Accuracy: 0.015313 	10
Accuracy: 0.0154307 	11
Accuracy: 0.015549 	12
Accuracy: 0.0156681 	13
Accuracy: 0.0157879 	14
Accuracy: 0.0159084 	15
Accuracy: 0.0160297 	16
Accuracy: 0.0161517 	17
Accuracy: 0.0162744 	18
Accuracy: 0.0163979 	19
Accuracy: 0.016522 	20
Accuracy: 0.0166469 	21
Accuracy: 0.0167726 	22
Accuracy: 0.016899 	23
Accuracy: 0.0170261 	24
Accuracy: 0.017154 	25
Accuracy: 0.0172826 	26
Accuracy: 0.0174119 	27
Accuracy: 0.017542 	28
Accuracy: 0.0176729 	29
Accuracy: 0.0178045 	30
Accuracy: 0.0179369 	31
Accuracy: 0.01807 	32
Accuracy: 0.0182038 	33
Accuracy: 0.0183385 	34
Accuracy: 0.0184739 	35
Accuracy: 0.01861 	36
Accuracy: 0.018747 	37
Accuracy: 0.0188847 	38
Accuracy: 0.0190232 	39
Accuracy: 0.0191624 	40
Accuracy: 0.0193025 	41
Accuracy: 0.0194433 	42
Accuracy: 0.0195849 	43
Accuracy: 0.0197273 	44
Accuracy: 0.0198704 	45
Accuracy: 0.0200144 	46
Accuracy: 0.0201592 	47
Accuracy: 0.0203048 	48
Accuracy: 0.0204511 	49
Accuracy: 0.0205983 	50
Accuracy: 0.0207463 	51
Accuracy: 0.0208951 	52
Accuracy: 0.0210447 	53
Accuracy: 0.0211952 	54
Accuracy: 0.0213465 	55
Accuracy: 0.0214985 	56
Accuracy: 0.0216515 	57
Accuracy: 0.0218052 	58
Accuracy: 0.0219598 	59
Accuracy: 0.0221153 	60
Accuracy: 0.0222715 	61
Accuracy: 0.0224287 	62
Accuracy: 0.0225867 	63
Accuracy: 0.0227455 	64
Accuracy: 0.0229052 	65
Accuracy: 0.0230658 	66
Accuracy: 0.0232272 	67
Accuracy: 0.0233895 	68
Accuracy: 0.0235535 	69
Accuracy: 0.023719 	70
Accuracy: 0.0238869 	71
Accuracy: 0.0240571 	72
Accuracy: 0.0242296 	73
Accuracy: 0.0244038 	74
Accuracy: 0.0245791 	75
Accuracy: 0.0247561 	76
Accuracy: 0.0249346 	77
Accuracy: 0.0251152 	78
Accuracy: 0.0252985 	79
Accuracy: 0.0254828 	80
Accuracy: 0.0256687 	81
Accuracy: 0.025857 	82
Accuracy: 0.0260465 	83
Accuracy: 0.0262383 	84
Accuracy: 0.0264316 	85
Accuracy: 0.0266263 	86
Accuracy: 0.0268221 	87
Accuracy: 0.0270191 	88
Accuracy: 0.0272175 	89
Accuracy: 0.0274176 	90
Accuracy: 0.0276215 	91
Accuracy: 0.0278284 	92
Accuracy: 0.0280369 	93
Accuracy: 0.0282466 	94
Accuracy: 0.0284579 	95
Accuracy: 0.0286707 	96
Accuracy: 0.0288851 	97
Accuracy: 0.0291017 	98
Accuracy: 0.0293195 	99
[(0.03175395, 83), (0.015308196, 99), (0.030879561, 53), (0.033152718, 52), (0.048278533, 51), (0.029319521, 99)]
Interp bead indices: 
[1, 2, 3, 4, 5, 6]
[4, 5, 6, 7]
Accuracy: 0.0108723
Final bead error: 0.0108723
Accuracy: 0.0198618
Accuracy: 0.0154325
Accuracy: 0.0143733
Final bead error: 0.0143733
[True, True, True, True, True, True, True, True, True, True]
Accuracy: 0.0137014 	0
Accuracy: 0.0136473 	1
Accuracy: 0.0135972 	2
Accuracy: 0.013551 	3
Accuracy: 0.0135085 	4
Accuracy: 0.0134695 	5
Accuracy: 0.0134338 	6
Accuracy: 0.0134012 	7
Accuracy: 0.0133716 	8
Accuracy: 0.0133447 	9
Accuracy: 0.0133204 	10
Accuracy: 0.0132986 	11
Accuracy: 0.013279 	12
Accuracy: 0.0132615 	13
Accuracy: 0.013246 	14
Accuracy: 0.0132323 	15
Accuracy: 0.0132201 	16
Accuracy: 0.0132094 	17
Accuracy: 0.0132001 	18
Accuracy: 0.0131919 	19
Accuracy: 0.0131848 	20
Accuracy: 0.0131786 	21
Accuracy: 0.0131731 	22
Accuracy: 0.0131682 	23
Accuracy: 0.0131639 	24
Accuracy: 0.0131599 	25
Accuracy: 0.0131562 	26
Accuracy: 0.0131527 	27
Accuracy: 0.0131491 	28
Accuracy: 0.0131455 	29
Accuracy: 0.0131416 	30
Accuracy: 0.0131375 	31
Accuracy: 0.0131329 	32
Accuracy: 0.0131278 	33
Accuracy: 0.0131222 	34
Accuracy: 0.0131159 	35
Accuracy: 0.0131089 	36
Accuracy: 0.013101 	37
Accuracy: 0.0130922 	38
Accuracy: 0.0130824 	39
Accuracy: 0.0130715 	40
Accuracy: 0.0130596 	41
Accuracy: 0.0130465 	42
Accuracy: 0.0130322 	43
Accuracy: 0.0130166 	44
Accuracy: 0.0129997 	45
Accuracy: 0.0129814 	46
Accuracy: 0.0129617 	47
Accuracy: 0.0129407 	48
Accuracy: 0.0129181 	49
Accuracy: 0.0128942 	50
Accuracy: 0.0128687 	51
Accuracy: 0.0128418 	52
Accuracy: 0.0128134 	53
Accuracy: 0.0127834 	54
Accuracy: 0.012752 	55
Accuracy: 0.0127191 	56
Accuracy: 0.0126847 	57
Accuracy: 0.0126489 	58
Accuracy: 0.0126117 	59
Accuracy: 0.012573 	60
Accuracy: 0.012533 	61
Accuracy: 0.0124916 	62
Accuracy: 0.012449 	63
Accuracy: 0.0124052 	64
Accuracy: 0.0123603 	65
Accuracy: 0.0123142 	66
Accuracy: 0.0122671 	67
Accuracy: 0.012219 	68
Accuracy: 0.0121701 	69
Accuracy: 0.0121204 	70
Accuracy: 0.01207 	71
Accuracy: 0.012019 	72
Accuracy: 0.0119676 	73
Accuracy: 0.0119158 	74
Accuracy: 0.0118637 	75
Accuracy: 0.0118115 	76
Accuracy: 0.0117593 	77
Accuracy: 0.0117072 	78
Accuracy: 0.0116554 	79
Accuracy: 0.0116039 	80
Accuracy: 0.0115531 	81
Accuracy: 0.011503 	82
Accuracy: 0.0114539 	83
Accuracy: 0.0114058 	84
Accuracy: 0.0113589 	85
Accuracy: 0.0113135 	86
Accuracy: 0.0112698 	87
Accuracy: 0.011228 	88
Accuracy: 0.0111882 	89
Accuracy: 0.0111506 	90
Accuracy: 0.0111156 	91
Accuracy: 0.0110833 	92
Accuracy: 0.0110539 	93
Accuracy: 0.0110278 	94
Accuracy: 0.0110051 	95
Accuracy: 0.0109862 	96
Accuracy: 0.0109713 	97
Accuracy: 0.0109606 	98
Accuracy: 0.0109545 	99
Accuracy: 0.0111349 	0
Accuracy: 0.011131 	1
Accuracy: 0.0111315 	2
Accuracy: 0.0111362 	3
Accuracy: 0.0111449 	4
Accuracy: 0.0111574 	5
Accuracy: 0.0111735 	6
Accuracy: 0.0111929 	7
Accuracy: 0.0112156 	8
Accuracy: 0.0112412 	9
Accuracy: 0.0112696 	10
Accuracy: 0.0113006 	11
Accuracy: 0.011334 	12
Accuracy: 0.0113697 	13
Accuracy: 0.0114075 	14
Accuracy: 0.0114471 	15
Accuracy: 0.0114885 	16
Accuracy: 0.0115314 	17
Accuracy: 0.0115758 	18
Accuracy: 0.0116214 	19
Accuracy: 0.0116681 	20
Accuracy: 0.0117157 	21
Accuracy: 0.0117642 	22
Accuracy: 0.0118134 	23
Accuracy: 0.0118632 	24
Accuracy: 0.0119134 	25
Accuracy: 0.0119638 	26
Accuracy: 0.0120145 	27
Accuracy: 0.0120652 	28
Accuracy: 0.0121159 	29
Accuracy: 0.0121665 	30
Accuracy: 0.0122169 	31
Accuracy: 0.0122669 	32
Accuracy: 0.0123165 	33
Accuracy: 0.0123655 	34
Accuracy: 0.012414 	35
Accuracy: 0.0124618 	36
Accuracy: 0.0125088 	37
Accuracy: 0.012555 	38
Accuracy: 0.0126003 	39
Accuracy: 0.0126447 	40
Accuracy: 0.0126881 	41
Accuracy: 0.0127305 	42
Accuracy: 0.0127717 	43
Accuracy: 0.0128118 	44
Accuracy: 0.0128508 	45
Accuracy: 0.0128886 	46
Accuracy: 0.0129251 	47
Accuracy: 0.0129605 	48
Accuracy: 0.0129946 	49
Accuracy: 0.0130274 	50
Accuracy: 0.013059 	51
Accuracy: 0.0130894 	52
Accuracy: 0.0131185 	53
Accuracy: 0.0131464 	54
Accuracy: 0.013173 	55
Accuracy: 0.0131985 	56
Accuracy: 0.0132228 	57
Accuracy: 0.013246 	58
Accuracy: 0.0132681 	59
Accuracy: 0.0132892 	60
Accuracy: 0.0133094 	61
Accuracy: 0.0133286 	62
Accuracy: 0.0133469 	63
Accuracy: 0.0133645 	64
Accuracy: 0.0133813 	65
Accuracy: 0.0133975 	66
Accuracy: 0.0134132 	67
Accuracy: 0.0134284 	68
Accuracy: 0.0134432 	69
Accuracy: 0.0134578 	70
Accuracy: 0.0134722 	71
Accuracy: 0.0134866 	72
Accuracy: 0.0135011 	73
Accuracy: 0.0135158 	74
Accuracy: 0.0135308 	75
Accuracy: 0.0135464 	76
Accuracy: 0.0135626 	77
Accuracy: 0.0135796 	78
Accuracy: 0.0135976 	79
Accuracy: 0.0136167 	80
Accuracy: 0.013637 	81
Accuracy: 0.0136589 	82
Accuracy: 0.0136824 	83
Accuracy: 0.0137077 	84
Accuracy: 0.0137351 	85
Accuracy: 0.0137647 	86
Accuracy: 0.0137968 	87
Accuracy: 0.0138316 	88
Accuracy: 0.0138693 	89
Accuracy: 0.0139101 	90
Accuracy: 0.0139543 	91
Accuracy: 0.0140022 	92
Accuracy: 0.014054 	93
Accuracy: 0.0141099 	94
Accuracy: 0.0141702 	95
Accuracy: 0.0142352 	96
Accuracy: 0.0143051 	97
Accuracy: 0.0143803 	98
Accuracy: 0.014461 	99
Accuracy: 0.0147008 	0
Accuracy: 0.0147764 	1
Accuracy: 0.0148611 	2
Accuracy: 0.0149541 	3
Accuracy: 0.0150549 	4
Accuracy: 0.0151629 	5
Accuracy: 0.0152775 	6
Accuracy: 0.0153982 	7
Accuracy: 0.0155245 	8
Accuracy: 0.0156558 	9
Accuracy: 0.0157916 	10
Accuracy: 0.0159313 	11
Accuracy: 0.0160745 	12
Accuracy: 0.0162207 	13
Accuracy: 0.0163694 	14
Accuracy: 0.0165201 	15
Accuracy: 0.0166723 	16
Accuracy: 0.0168256 	17
Accuracy: 0.0169796 	18
Accuracy: 0.0171338 	19
Accuracy: 0.0172878 	20
Accuracy: 0.0174412 	21
Accuracy: 0.0175937 	22
Accuracy: 0.0177447 	23
Accuracy: 0.017894 	24
Accuracy: 0.0180412 	25
Accuracy: 0.0181858 	26
Accuracy: 0.0183277 	27
Accuracy: 0.0184665 	28
Accuracy: 0.0186018 	29
Accuracy: 0.0187333 	30
Accuracy: 0.0188608 	31
Accuracy: 0.0189839 	32
Accuracy: 0.0191025 	33
Accuracy: 0.0192161 	34
Accuracy: 0.0193247 	35
Accuracy: 0.019428 	36
Accuracy: 0.0195257 	37
Accuracy: 0.0196176 	38
Accuracy: 0.0197035 	39
Accuracy: 0.0197834 	40
Accuracy: 0.0198569 	41
Accuracy: 0.0199239 	42
Accuracy: 0.0199844 	43
Accuracy: 0.020038 	44
Accuracy: 0.0200849 	45
Accuracy: 0.0201248 	46
Accuracy: 0.0201576 	47
Accuracy: 0.0201833 	48
Accuracy: 0.0202017 	49
Accuracy: 0.020213 	50
Accuracy: 0.0202169 	51
Accuracy: 0.0202136 	52
Accuracy: 0.0202028 	53
Accuracy: 0.0201848 	54
Accuracy: 0.0201595 	55
Accuracy: 0.020127 	56
Accuracy: 0.0200872 	57
Accuracy: 0.0200402 	58
Accuracy: 0.0199861 	59
Accuracy: 0.019925 	60
Accuracy: 0.0198571 	61
Accuracy: 0.0197823 	62
Accuracy: 0.0197009 	63
Accuracy: 0.019613 	64
Accuracy: 0.0195188 	65
Accuracy: 0.0194183 	66
Accuracy: 0.019312 	67
Accuracy: 0.0191998 	68
Accuracy: 0.019082 	69
Accuracy: 0.018959 	70
Accuracy: 0.0188309 	71
Accuracy: 0.018698 	72
Accuracy: 0.0185606 	73
Accuracy: 0.0184189 	74
Accuracy: 0.0182734 	75
Accuracy: 0.0181243 	76
Accuracy: 0.017972 	77
Accuracy: 0.0178168 	78
Accuracy: 0.0176591 	79
Accuracy: 0.0174993 	80
Accuracy: 0.0173378 	81
Accuracy: 0.0171751 	82
Accuracy: 0.0170115 	83
Accuracy: 0.0168476 	84
Accuracy: 0.0166838 	85
Accuracy: 0.0165206 	86
Accuracy: 0.0163585 	87
Accuracy: 0.016198 	88
Accuracy: 0.0160398 	89
Accuracy: 0.0158843 	90
Accuracy: 0.015732 	91
Accuracy: 0.0155838 	92
Accuracy: 0.01544 	93
Accuracy: 0.0153014 	94
Accuracy: 0.0151686 	95
Accuracy: 0.0150422 	96
Accuracy: 0.014923 	97
Accuracy: 0.0148116 	98
Accuracy: 0.0147088 	99
Accuracy: 0.0147082 	0
Accuracy: 0.0148162 	1
Accuracy: 0.014925 	2
Accuracy: 0.0150342 	3
Accuracy: 0.0151439 	4
Accuracy: 0.0152539 	5
Accuracy: 0.015364 	6
Accuracy: 0.0154741 	7
Accuracy: 0.015584 	8
Accuracy: 0.0156937 	9
Accuracy: 0.015803 	10
Accuracy: 0.0159118 	11
Accuracy: 0.01602 	12
Accuracy: 0.0161274 	13
Accuracy: 0.0162339 	14
Accuracy: 0.0163395 	15
Accuracy: 0.0164439 	16
Accuracy: 0.0165472 	17
Accuracy: 0.0166492 	18
Accuracy: 0.0167498 	19
Accuracy: 0.0168489 	20
Accuracy: 0.0169464 	21
Accuracy: 0.0170422 	22
Accuracy: 0.0171363 	23
Accuracy: 0.0172285 	24
Accuracy: 0.0173188 	25
Accuracy: 0.017407 	26
Accuracy: 0.0174932 	27
Accuracy: 0.0175773 	28
Accuracy: 0.0176591 	29
Accuracy: 0.0177386 	30
Accuracy: 0.0178157 	31
Accuracy: 0.0178904 	32
Accuracy: 0.0179627 	33
Accuracy: 0.0180324 	34
Accuracy: 0.0180996 	35
Accuracy: 0.0181641 	36
Accuracy: 0.0182259 	37
Accuracy: 0.0182851 	38
Accuracy: 0.0183411 	39
Accuracy: 0.0183939 	40
Accuracy: 0.0184435 	41
Accuracy: 0.0184897 	42
Accuracy: 0.0185323 	43
Accuracy: 0.0185713 	44
Accuracy: 0.0186067 	45
Accuracy: 0.0186385 	46
Accuracy: 0.0186667 	47
Accuracy: 0.0186912 	48
Accuracy: 0.0187118 	49
Accuracy: 0.0187287 	50
Accuracy: 0.0187412 	51
Accuracy: 0.0187495 	52
Accuracy: 0.0187539 	53
Accuracy: 0.0187542 	54
Accuracy: 0.0187502 	55
Accuracy: 0.0187423 	56
Accuracy: 0.0187302 	57
Accuracy: 0.0187133 	58
Accuracy: 0.0186919 	59
Accuracy: 0.0186657 	60
Accuracy: 0.0186351 	61
Accuracy: 0.0185999 	62
Accuracy: 0.0185599 	63
Accuracy: 0.0185153 	64
Accuracy: 0.0184662 	65
Accuracy: 0.0184125 	66
Accuracy: 0.0183542 	67
Accuracy: 0.0182913 	68
Accuracy: 0.0182239 	69
Accuracy: 0.018152 	70
Accuracy: 0.0180759 	71
Accuracy: 0.0179949 	72
Accuracy: 0.0179092 	73
Accuracy: 0.017819 	74
Accuracy: 0.017724 	75
Accuracy: 0.0176247 	76
Accuracy: 0.0175208 	77
Accuracy: 0.0174128 	78
Accuracy: 0.0173006 	79
Accuracy: 0.0171845 	80
Accuracy: 0.0170647 	81
Accuracy: 0.0169412 	82
Accuracy: 0.0168144 	83
Accuracy: 0.0166845 	84
Accuracy: 0.016552 	85
Accuracy: 0.0164174 	86
Accuracy: 0.0162811 	87
Accuracy: 0.0161437 	88
Accuracy: 0.0160054 	89
Accuracy: 0.0158666 	90
Accuracy: 0.0157279 	91
Accuracy: 0.0155898 	92
Accuracy: 0.0154533 	93
Accuracy: 0.0153204 	94
Accuracy: 0.0151921 	95
Accuracy: 0.0150679 	96
Accuracy: 0.0149512 	97
Accuracy: 0.0148471 	98
Accuracy: 0.0147585 	99
[(0.013701362, 0), (0.014461015, 99), (0.020216927, 51), (0.018754244, 54)]
Interp bead indices: 
[4, 5, 6, 7]
1
2
Thresh: 0.03
Comps: 1
***
15.718850784
1.82471144944

In [60]:
for b in xrange(len(tests[0].AllBeads)-1):
    e = InterpBeadError(tests[0].AllBeads[b][0],tests[0].AllBeads[b][1], tests[0].AllBeads[b+1][0], tests[0].AllBeads[b+1][1])


Accuracy: 0.0328678 	0 0.377708 0.318923
Accuracy: 0.0325319 	1 0.37981 0.31657
Accuracy: 0.0322168 	2 0.381912 0.314217
Accuracy: 0.031929 	3 0.384015 0.311864
Accuracy: 0.0316662 	4 0.386117 0.309511
Accuracy: 0.0314231 	5 0.388219 0.307158
Accuracy: 0.0311933 	6 0.390321 0.304805
Accuracy: 0.0309831 	7 0.392424 0.302452
Accuracy: 0.0307966 	8 0.394526 0.300099
Accuracy: 0.0306282 	9 0.396628 0.297746
Accuracy: 0.0304846 	10 0.39873 0.295393
Accuracy: 0.0303562 	11 0.400833 0.29304
Accuracy: 0.0302359 	12 0.402935 0.290687
Accuracy: 0.0301275 	13 0.405037 0.288334
Accuracy: 0.0300331 	14 0.40714 0.285981
Accuracy: 0.0299499 	15 0.409242 0.283628
Accuracy: 0.0298774 	16 0.411344 0.281275
Accuracy: 0.0298219 	17 0.413446 0.278922
Accuracy: 0.029779 	18 0.415549 0.276569
Accuracy: 0.0297442 	19 0.417651 0.274216
Accuracy: 0.02972 	20 0.419753 0.271863
Accuracy: 0.0297074 	21 0.421855 0.26951
Accuracy: 0.0297017 	22 0.423958 0.267157
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Accuracy: 0.0195182 	58 0.916381 0.482828
Accuracy: 0.0197083 	59 0.916883 0.482623
Accuracy: 0.019899 	60 0.917385 0.482417
Accuracy: 0.0200903 	61 0.917887 0.482212
Accuracy: 0.0202824 	62 0.918389 0.482007
Accuracy: 0.0204751 	63 0.91889 0.481802
Accuracy: 0.0206686 	64 0.919392 0.481597
Accuracy: 0.0208628 	65 0.919894 0.481392
Accuracy: 0.0210577 	66 0.920396 0.481187
Accuracy: 0.0212535 	67 0.920898 0.480982
Accuracy: 0.0214501 	68 0.9214 0.480777
Accuracy: 0.0216475 	69 0.921902 0.480572
Accuracy: 0.0218457 	70 0.922404 0.480367
Accuracy: 0.0220448 	71 0.922905 0.480162
Accuracy: 0.0222447 	72 0.923407 0.479957
Accuracy: 0.0224456 	73 0.923909 0.479752
Accuracy: 0.0226473 	74 0.924411 0.479547
Accuracy: 0.0228499 	75 0.924913 0.479342
Accuracy: 0.0230535 	76 0.925415 0.479137
Accuracy: 0.0232582 	77 0.925917 0.478932
Accuracy: 0.0234641 	78 0.926419 0.478727
Accuracy: 0.0236711 	79 0.926921 0.478522
Accuracy: 0.0238793 	80 0.927422 0.478317
Accuracy: 0.0240888 	81 0.927924 0.478112
Accuracy: 0.0242995 	82 0.928426 0.477907
Accuracy: 0.0245114 	83 0.928928 0.477702
Accuracy: 0.0247247 	84 0.92943 0.477497
Accuracy: 0.0249396 	85 0.929932 0.477292
Accuracy: 0.0251559 	86 0.930434 0.477087
Accuracy: 0.0253737 	87 0.930936 0.476882
Accuracy: 0.0255929 	88 0.931437 0.476677
Accuracy: 0.0258137 	89 0.931939 0.476472
Accuracy: 0.0260361 	90 0.932441 0.476267
Accuracy: 0.0262602 	91 0.932943 0.476062
Accuracy: 0.026486 	92 0.933445 0.475857
Accuracy: 0.0267138 	93 0.933947 0.475652
Accuracy: 0.0269433 	94 0.934449 0.475447
Accuracy: 0.0271748 	95 0.934951 0.475242
Accuracy: 0.0274081 	96 0.935452 0.475037
Accuracy: 0.0276438 	97 0.935954 0.474832
Accuracy: 0.0278816 	98 0.936456 0.474627
Accuracy: 0.0281214 	99 0.936958 0.474422

In [58]:



Out[58]:
0.31892276

In [ ]: