MNIST dataset classification problem

https://github.com/abulbasar/data/tree/master/mnist



In [4]:

    
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

pd.options.display.max_columns=1000



In [5]:

    
df_train = pd.read_csv("/data/MNIST/mnist_train.csv", header = None)
df_train.head()



In [6]:

    
df_train.shape









    Out[6]:





(60000, 785)



In [7]:

    
28 * 28









    Out[7]:





784



In [9]:

    
df_train.iloc[0, 1:].shape









    Out[9]:





(784,)



In [12]:

    
df_train.iloc[0, 1:].values.reshape((28,28)).shape









    Out[12]:





(28, 28)



In [15]:

    
plt.imshow(df_train.iloc[0, 1:].values.reshape((28,28)), cmap="gray")
plt.title("t: %d" % (df_train.iloc[0, 0]))









    Out[15]:





Text(0.5, 1.0, 't: 5')



In [14]:

    
df_train.iloc[0, 0]









    Out[14]:





5



In [19]:

    
fig, _ = plt.subplots(10, 10, figsize = (20, 20))

for i, ax in enumerate(fig.axes):
    ax.imshow(df_train.iloc[i, 1:].values.reshape((28,28)), cmap="gray")
    ax.set_title("t: %d" % (df_train.iloc[i, 0]))
    
    
plt.tight_layout()



In [21]:

    
X_train = df_train.iloc[:, 1:].values
y_train = df_train.iloc[:, 0].values



In [23]:

    
a = X_train.flatten()
a.min(),a.max()









    Out[23]:





(0, 255)



In [25]:

    
plt.hist(a, bins = 50);



In [ ]:

Standard scaling

z = (x - mean(x))/ std(x), x = each feature column



In [28]:

    
len(a[a>0])/len(a)









    Out[28]:





0.19120229591836735



In [30]:

    
X_train[0].shape









    Out[30]:





(784,)



In [32]:

    
X_train[0].mean(), X_train[0].std()









    Out[32]:





(35.108418367346935, 79.64882892760731)

Minmax scaling

z = (x - min(x))/ (max(x) - min(x)). , x = each feature column

z = x / 255



In [38]:

    
X_train = df_train.iloc[:, 1:].values/255.0
y_train = df_train.iloc[:, 0].values



In [34]:

    
a = X_train.flatten()
a.min(),a.max()









    Out[34]:





(0.0, 0.99609375)



In [35]:

    
plt.hist(a, bins = 50);



In [86]:

    
df_test = pd.read_csv("/data/MNIST/mnist_test.csv", header = None)
df_test.head()



In [87]:

    
X_test = df_test.iloc[:, 1:].values/255.0
y_test = df_test.iloc[:, 0].values



In [40]:

    
from sklearn import *



In [88]:

    
est = linear_model.SGDClassifier(n_jobs=8, tol=1e-5, eta0 = 0.15,  
                                 learning_rate = "invscaling", 
                                 alpha = 0.01, max_iter= 100)
est.fit(X_train, y_train)
print("accuracy", est.score(X_test, y_test), "iterations:", est.n_iter_)









    



accuracy 0.9082 iterations: 61



In [44]:

    
est.coef_.shape









    Out[44]:





(10, 784)



In [89]:

    
X_test.shape









    Out[89]:





(10000, 784)



In [47]:

    
pd.Series(y_train).nunique()









    Out[47]:





10



In [50]:

    
pd.Series(y_train).value_counts().sort_index().plot.bar()









    Out[50]:





<matplotlib.axes._subplots.AxesSubplot at 0x12298a650>



In [52]:

    
est.coef_[0].shape









    Out[52]:





(784,)



In [55]:

    
est.coef_[0].min(), est.coef_[0].max()









    Out[55]:





(-0.20998576864335874, 0.11819429479848767)



In [57]:

    
plt.imshow(est.coef_[0].reshape((28, 28)), cmap="gray")









    Out[57]:





<matplotlib.image.AxesImage at 0x122c12510>



In [60]:

    
import tensorflow as tf
import keras



In [90]:

    
Y_train = keras.utils.to_categorical(y_train)
Y_test = keras.utils.to_categorical(y_test)
Y_train[:10,:]









    Out[90]:





array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],
       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]], dtype=float32)



In [91]:

    
y_train[:10]









    Out[91]:





array([5, 0, 4, 1, 9, 2, 1, 3, 1, 4])



In [92]:

    
from time import time
tensor_board = keras.callbacks.TensorBoard("/tmp/tf/%d" % time())

adam = keras.optimizers.Adam(lr = 0.005)

model = keras.Sequential([
    keras.layers.InputLayer(input_shape=(784, )),
    keras.layers.Dense(400, activation="relu"),
    keras.layers.Dense(100, activation="relu"),
    keras.layers.Dense(10, activation="softmax")
])


model.summary()

model.compile(optimizer = adam, loss = keras.losses.categorical_crossentropy, metrics = ["acc"])

model.fit(X_train, Y_train, batch_size = 256, epochs=10
          , validation_data = (X_test, Y_test), callbacks = [tensor_board])









    



_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_14 (Dense)             (None, 400)               314000    
_________________________________________________________________
dense_15 (Dense)             (None, 100)               40100     
_________________________________________________________________
dense_16 (Dense)             (None, 10)                1010      
=================================================================
Total params: 355,110
Trainable params: 355,110
Non-trainable params: 0
_________________________________________________________________
Train on 60000 samples, validate on 10000 samples
Epoch 1/10
60000/60000 [==============================] - 3s 50us/step - loss: 0.2288 - acc: 0.9287 - val_loss: 0.1036 - val_acc: 0.9679
Epoch 2/10
60000/60000 [==============================] - 3s 47us/step - loss: 0.0891 - acc: 0.9718 - val_loss: 0.1086 - val_acc: 0.9674
Epoch 3/10
60000/60000 [==============================] - 3s 47us/step - loss: 0.0614 - acc: 0.9799 - val_loss: 0.0856 - val_acc: 0.9744
Epoch 4/10
60000/60000 [==============================] - 3s 46us/step - loss: 0.0483 - acc: 0.9846 - val_loss: 0.0806 - val_acc: 0.9767
Epoch 5/10
60000/60000 [==============================] - 3s 46us/step - loss: 0.0421 - acc: 0.9857 - val_loss: 0.0916 - val_acc: 0.9743
Epoch 6/10
60000/60000 [==============================] - 3s 46us/step - loss: 0.0362 - acc: 0.9879 - val_loss: 0.0836 - val_acc: 0.9758
Epoch 7/10
60000/60000 [==============================] - 3s 46us/step - loss: 0.0291 - acc: 0.9907 - val_loss: 0.0826 - val_acc: 0.9773
Epoch 8/10
60000/60000 [==============================] - 3s 46us/step - loss: 0.0253 - acc: 0.9922 - val_loss: 0.0780 - val_acc: 0.9801
Epoch 9/10
60000/60000 [==============================] - 3s 48us/step - loss: 0.0209 - acc: 0.9933 - val_loss: 0.1002 - val_acc: 0.9742
Epoch 10/10
60000/60000 [==============================] - 3s 47us/step - loss: 0.0262 - acc: 0.9917 - val_loss: 0.0968 - val_acc: 0.9774






    Out[92]:





<keras.callbacks.History at 0x16c004c10>



In [93]:

    
y_test_pred = model.predict_classes(X_test)



In [94]:

    
y_test_pred.shape









    Out[94]:





(10000,)



In [81]:

    
from sklearn import *



In [96]:

    
print(metrics.classification_report(y_test, y_test_pred ))









    



              precision    recall  f1-score   support

           0       0.99      0.99      0.99       980
           1       1.00      0.99      0.99      1135
           2       0.99      0.97      0.98      1032
           3       0.97      0.98      0.98      1010
           4       0.98      0.98      0.98       982
           5       0.99      0.97      0.98       892
           6       0.97      0.99      0.98       958
           7       0.98      0.96      0.97      1028
           8       0.96      0.98      0.97       974
           9       0.96      0.97      0.96      1009

    accuracy                           0.98     10000
   macro avg       0.98      0.98      0.98     10000
weighted avg       0.98      0.98      0.98     10000



In [97]:



In [102]:

    
errors = y_test != y_test_pred

fig, _ = plt.subplots(10, 10, figsize = (20, 20))

for i, ax in enumerate(fig.axes):
    ax.imshow(X_test[errors][i, :].reshape((28,28)), cmap="gray")
    ax.set_title("t: %d, p: %d" % (y_test[errors][i], y_test_pred[errors][i]))
    
plt.tight_layout()



In [103]:

    
from skimage.io import imread



In [104]:

    
elephant = imread("/Users/abasar/Downloads/elephant.jpg")



In [106]:

    
elephant.shape









    Out[106]:





(1363, 2048, 3)



In [107]:

    
plt.imshow(elephant)









    Out[107]:





<matplotlib.image.AxesImage at 0x124819a50>



In [118]:

    
import cv2



In [120]:

    
elephant_gray = cv2.cvtColor(elephant, cv2.COLOR_BGR2GRAY)



In [121]:

    
plt.imshow(elephant_gray, cmap="gray")









    Out[121]:





<matplotlib.image.AxesImage at 0x123df1b50>



In [122]:

    
elephant_gray.shape









    Out[122]:





(1363, 2048)



In [117]:

    
plt.imshow(elephant[:,:,2], cmap="gray")









    Out[117]:





<matplotlib.image.AxesImage at 0x123466ed0>



In [113]:

    
pd.Series(elephant[:,:,2].flatten()).plot.hist(bins = 50)









    Out[113]:





<matplotlib.axes._subplots.AxesSubplot at 0x123b38d10>



In [114]:

    
pd.Series(elephant.flatten()).plot.hist(bins = 50)









    Out[114]:





<matplotlib.axes._subplots.AxesSubplot at 0x123a20fd0>



In [115]:

    
pd.Series(elephant.flatten()).describe()









    Out[115]:





count    8.374272e+06
mean     1.287571e+02
std      8.176032e+01
min      0.000000e+00
25%      5.400000e+01
50%      1.150000e+02
75%      2.160000e+02
max      2.550000e+02
dtype: float64



In [124]:

    
elepant_png = imread("/Users/abasar/Downloads/elephants_PNG18793.png")



In [125]:

    
elepant_png.shape









    Out[125]:





(778, 1097, 4)



In [128]:

    
plt.hist(elepant_png[:,:,3].flatten(), bins = 50)









    Out[128]:





(array([4.50264e+05, 7.53000e+02, 5.03000e+02, 3.57000e+02, 3.54000e+02,
        2.51000e+02, 2.33000e+02, 1.79000e+02, 1.94000e+02, 1.74000e+02,
        2.18000e+02, 1.82000e+02, 1.75000e+02, 1.67000e+02, 1.36000e+02,
        1.50000e+02, 1.29000e+02, 1.44000e+02, 1.27000e+02, 1.52000e+02,
        1.69000e+02, 1.33000e+02, 1.24000e+02, 1.40000e+02, 1.27000e+02,
        1.38000e+02, 1.30000e+02, 1.36000e+02, 1.41000e+02, 1.36000e+02,
        1.64000e+02, 1.63000e+02, 1.47000e+02, 1.61000e+02, 1.70000e+02,
        1.71000e+02, 1.79000e+02, 2.10000e+02, 1.88000e+02, 1.98000e+02,
        2.57000e+02, 1.92000e+02, 2.71000e+02, 2.77000e+02, 2.87000e+02,
        3.42000e+02, 4.03000e+02, 4.97000e+02, 6.83000e+02, 3.92290e+05]),
 array([  0. ,   5.1,  10.2,  15.3,  20.4,  25.5,  30.6,  35.7,  40.8,
         45.9,  51. ,  56.1,  61.2,  66.3,  71.4,  76.5,  81.6,  86.7,
         91.8,  96.9, 102. , 107.1, 112.2, 117.3, 122.4, 127.5, 132.6,
        137.7, 142.8, 147.9, 153. , 158.1, 163.2, 168.3, 173.4, 178.5,
        183.6, 188.7, 193.8, 198.9, 204. , 209.1, 214.2, 219.3, 224.4,
        229.5, 234.6, 239.7, 244.8, 249.9, 255. ]),
 <a list of 50 Patch objects>)



In [129]:

    
pd.Series(elepant_png[:,:,3].flatten()).describe()









    Out[129]:





count    853466.000000
mean        118.869841
std         126.745542
min           0.000000
25%           0.000000
50%           0.000000
75%         255.000000
max         255.000000
dtype: float64



In [130]:

    
pd.Series(elepant_png[:,:,3].flatten()).value_counts()









    Out[130]:





0      447469
255    390764
1        1330
2         573
254       562
        ...  
112        19
124        18
79         18
120        18
133        17
Length: 256, dtype: int64



In [133]:

    
X_train_3d = X_train.reshape((60000, 28, 28, 1))
X_test_3d = X_test.reshape((-1, 28, 28, 1))
X_train_3d.shape, X_test_3d.shape









    Out[133]:





((60000, 28, 28, 1), (10000, 28, 28, 1))



In [134]:

    
from time import time
tensor_board = keras.callbacks.TensorBoard("/tmp/tf/cnn-%d" % time())

adam = keras.optimizers.Adam(lr = 0.005)

model = keras.Sequential([
    keras.layers.InputLayer(input_shape=(28, 28, 1)),
    keras.layers.Conv2D(32, kernel_size=(5, 5), padding="SAME", activation="relu"),
    keras.layers.MaxPool2D(pool_size=(2,2), padding="SAME"),
    keras.layers.Conv2D(64, kernel_size=(5, 5), padding="SAME", activation="relu"),
    keras.layers.MaxPool2D(pool_size=(2,2), padding="SAME"),
    
    keras.layers.Flatten(),
    keras.layers.Dropout(rate=0.5),
    
    keras.layers.Dense(400, activation="relu"),
    keras.layers.Dense(100, activation="relu"),
    keras.layers.Dense(10, activation="softmax")
])


model.summary()

model.compile(optimizer = adam, loss = keras.losses.categorical_crossentropy, metrics = ["acc"])

model.fit(X_train_3d, Y_train, batch_size = 256, epochs=10
          , validation_data = (X_test_3d, Y_test), callbacks = [tensor_board])









    



WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.






    



WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.






    



_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 28, 28, 32)        832       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 14, 14, 64)        51264     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 3136)              0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 3136)              0         
_________________________________________________________________
dense_17 (Dense)             (None, 400)               1254800   
_________________________________________________________________
dense_18 (Dense)             (None, 100)               40100     
_________________________________________________________________
dense_19 (Dense)             (None, 10)                1010      
=================================================================
Total params: 1,348,006
Trainable params: 1,348,006
Non-trainable params: 0
_________________________________________________________________
Train on 60000 samples, validate on 10000 samples
Epoch 1/10
60000/60000 [==============================] - 72s 1ms/step - loss: 0.2360 - acc: 0.9230 - val_loss: 0.0436 - val_acc: 0.9868
Epoch 2/10
60000/60000 [==============================] - 108s 2ms/step - loss: 0.0720 - acc: 0.9775 - val_loss: 0.0306 - val_acc: 0.9892
Epoch 3/10
60000/60000 [==============================] - 86s 1ms/step - loss: 0.0553 - acc: 0.9831 - val_loss: 0.0285 - val_acc: 0.9903
Epoch 4/10
60000/60000 [==============================] - 89s 1ms/step - loss: 0.0455 - acc: 0.9856 - val_loss: 0.0336 - val_acc: 0.9891
Epoch 5/10
60000/60000 [==============================] - 92s 2ms/step - loss: 0.0459 - acc: 0.9860 - val_loss: 0.0278 - val_acc: 0.9918
Epoch 6/10
60000/60000 [==============================] - 94s 2ms/step - loss: 0.0410 - acc: 0.9874 - val_loss: 0.0285 - val_acc: 0.9909
Epoch 7/10
60000/60000 [==============================] - 93s 2ms/step - loss: 0.0358 - acc: 0.9892 - val_loss: 0.0313 - val_acc: 0.9903
Epoch 8/10
60000/60000 [==============================] - 94s 2ms/step - loss: 0.0364 - acc: 0.9889 - val_loss: 0.0278 - val_acc: 0.9916
Epoch 9/10
60000/60000 [==============================] - 93s 2ms/step - loss: 0.0325 - acc: 0.9902 - val_loss: 0.0294 - val_acc: 0.9913
Epoch 10/10
60000/60000 [==============================] - 93s 2ms/step - loss: 0.0310 - acc: 0.9906 - val_loss: 0.0286 - val_acc: 0.9913






    Out[134]:





<keras.callbacks.History at 0x1d2641850>



In [137]:

    
y_test_pred = model.predict_classes(X_test_3d)

errors = y_test != y_test_pred

fig, _ = plt.subplots(10, 8, figsize = (20, 20))

for i, ax in enumerate(fig.axes):
    if(i < len(errors)):
        ax.imshow(X_test[errors][i, :].reshape((28,28)), cmap="gray")
        ax.set_title("t: %d, p: %d" % (y_test[errors][i], y_test_pred[errors][i]))

plt.tight_layout()



In [ ]:

	0	128	129	130	131	132	153	154	155	156	157	158	159	160	161	162	163	164	173	174	177	178	179	180	181	182	183	184	185	186	187	188	189	190	191	192	201	202	204	205	206	207	208	209	210	211	212	213	214	215	216	217	218	219	229	230	232	233	234	235	236	237	238	239	240	241	242	243	244	245	246	257	258	261	262	263	264	265	266	267	268	269	270	271	272	273	274	284	285	286	290	291	292	293	294	295	296	297	298	299	300	301	302	303	312	313	314	317	318	319	320	321	322	323	324	325	326	327	328	329	330	331	340	341	342	344	345	346	347	348	349	350	351	352	353	354	355	356	357	358	359	368	369	370	371	372	373	374	375	377	378	379	380	381	382	383	384	385	386	387	396	397	398	399	400	401	402	403	404	405	406	407	408	409	410	411	412	413	414	415	424	425	426	427	428	429	430	431	432	433	434	435	436	437	438	439	440	441	442	443	453	454	455	456	457	458	459	460	461	462	463	464	465	466	467	468	469	470	471	483	484	485	487	488	489	490	491	492	493	494	495	496	497	498	511	512	513	515	516	517	518	519	520	521	522	523	524	525	539	540	541	543	544	545	546	547	548	549	550	551	552	567	568	569	570	571	572	573	574	575	576	577	578	579	580	595	596	597	598	599	600	601	602	603	604	605	606	607	608	623	624	625	626	627	628	629	630	631	632	633	634	635	636	649	650	651	652	653	654	655	656	657	658	659	660	661	662	663	664	677	678	679	680	681	682	683	684	687	688	689	690	691	692	715	716	717	718	719	744	745	746	747
0	5	0	0	0	0	0	3	18	18	18	126	136	175	26	166	255	247	127	0	0	30	36	94	154	170	253	253	253	253	253	225	172	253	242	195	64	0	0	49	238	253	253	253	253	253	253	253	253	251	93	82	82	56	39	0	0	18	219	253	253	253	253	253	198	182	247	241	0	0	0	0	0	0	80	156	107	253	253	205	11	0	43	154	0	0	0	0	0	0	0	14	1	154	253	90	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	139	253	190	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	11	190	253	70	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	35	241	225	160	108	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	81	240	253	253	119	25	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	45	186	253	253	150	27	0	0	0	0	0	0	0	0	0	0	0	0	0	0	16	93	252	253	187	0	0	0	0	0	0	0	0	0	0	0	0	0	249	253	249	64	0	0	0	0	0	0	0	0	46	130	183	253	253	207	2	0	0	0	0	0	39	148	229	253	253	253	250	182	0	0	0	0	24	114	221	253	253	253	253	201	78	0	0	0	23	66	213	253	253	253	253	198	81	2	0	0	18	171	219	253	253	253	253	195	80	9	0	0	0	0	55	172	226	253	253	253	253	244	133	11	0	0	0	0	0	0	136	253	253	253	212	135	132	16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1	0	51	159	253	159	50	0	0	48	238	252	252	252	237	0	0	0	0	0	0	0	0	0	0	0	54	227	253	252	239	233	252	57	6	0	0	0	0	0	0	0	0	10	60	224	252	253	252	202	84	252	253	122	0	0	0	0	0	0	0	163	252	252	252	253	252	252	96	189	253	167	0	0	0	0	51	238	253	253	190	114	253	228	47	79	255	168	0	0	0	48	238	252	252	179	12	75	121	21	0	0	253	243	50	0	0	0	38	165	253	233	208	84	0	0	0	0	0	0	253	252	165	0	0	0	7	178	252	240	71	19	28	0	0	0	0	0	0	253	252	195	0	0	0	0	57	252	252	63	0	0	0	0	0	0	0	0	253	252	195	0	0	0	0	198	253	190	0	0	0	0	0	0	0	0	0	0	255	253	196	0	0	0	76	246	252	112	0	0	0	0	0	0	0	0	0	0	253	252	148	0	0	85	252	230	25	0	0	0	0	0	0	0	0	7	135	253	186	12	85	252	223	0	0	0	0	0	0	0	7	131	252	225	71	85	252	145	0	0	0	0	0	0	48	165	252	173	0	86	253	225	0	0	0	0	0	114	238	253	162	0	85	252	249	146	48	29	85	178	225	253	223	167	56	0	85	252	252	252	229	215	252	252	252	196	130	0	0	0	28	199	252	252	253	252	252	233	145	0	0	0	0	0	0	0	0	25	128	252	253	252	141	37	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	4	0	0	0	0	0	0	0	0	0	0	0	0	0	67	232	39	0	62	81	0	0	0	0	0	0	0	0	0	0	0	0	120	180	39	0	126	163	0	0	0	0	0	0	0	0	0	0	0	0	2	153	210	40	220	163	0	0	0	0	0	0	0	0	0	0	0	0	27	254	162	222	163	0	0	0	0	0	0	0	0	0	0	0	183	254	125	46	245	163	0	0	0	0	0	0	0	0	0	0	198	254	56	0	120	254	163	0	0	0	0	0	0	0	0	0	0	23	231	254	29	0	159	254	120	0	0	0	0	0	0	0	0	0	0	0	163	254	216	16	0	159	254	67	0	0	0	0	0	0	0	0	14	86	178	248	254	91	0	0	159	254	85	0	0	0	47	49	116	144	150	241	243	234	179	241	252	40	0	0	150	253	237	207	207	207	253	254	250	240	198	143	91	28	5	233	250	0	0	0	119	177	177	177	177	177	98	56	0	0	0	0	0	102	254	220	0	0	0	0	0	0	0	0	0	0	0	0	0	169	254	137	0	0	0	0	0	0	0	0	0	0	0	0	169	254	57	0	0	0	0	0	0	0	0	0	0	0	169	254	57	0	0	0	0	0	0	0	0	0	0	0	169	255	94	0	0	0	0	0	0	0	0	0	0	0	169	254	96	0	0	0	0	0	0	0	0	0	0	0	169	254	153	0	0	0	0	0	0	0	0	0	0	0	0	0	169	255	153	0	0	0	0	0	0	0	0	0	0	0	96	254	153	0	0	0	0	0	0	0	0	0
3	1	0	0	0	0	0	0	0	0	0	0	0	124	253	255	63	0	0	0	0	0	0	0	0	0	0	0	0	0	96	244	251	253	62	0	0	0	0	0	0	0	0	0	0	0	0	0	0	127	251	251	253	62	0	0	0	0	0	0	0	0	0	0	0	0	68	236	251	211	31	8	0	0	0	0	0	0	0	0	0	60	228	251	251	94	0	0	0	0	0	0	0	0	0	0	0	155	253	253	189	0	0	0	0	0	0	0	0	0	0	0	0	0	20	253	251	235	66	0	0	0	0	0	0	0	0	0	0	0	0	0	32	205	253	251	126	0	0	0	0	0	0	0	0	0	0	0	0	0	0	104	251	253	184	15	0	0	0	0	0	0	0	0	0	0	0	0	0	0	80	240	251	193	23	0	0	0	0	0	0	0	0	0	0	0	0	0	0	32	253	253	253	159	0	0	0	0	0	0	0	0	0	0	0	0	0	0	151	251	251	251	39	0	0	0	0	0	0	0	0	0	0	48	221	251	251	172	0	0	0	0	0	0	0	0	0	0	234	251	251	196	12	0	0	0	0	0	0	0	0	0	253	251	251	89	0	0	0	0	0	0	0	0	0	159	255	253	253	31	0	0	0	0	0	0	0	0	48	228	253	247	140	8	0	0	0	0	0	0	0	0	64	251	253	220	0	0	0	0	0	0	0	0	0	0	0	0	64	251	253	220	0	0	0	0	0	0	0	0	0	0	0	0	24	193	253	220	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	55	148	210	253	253	113	87	148	55	0	0	0	0	0	0	0	0	87	232	252	253	189	210	252	252	253	168	0	0	0	0	4	57	242	252	190	65	5	12	182	252	253	116	0	0	0	0	96	252	252	183	14	0	0	92	252	252	225	21	0	0	0	0	0	132	253	252	146	14	0	0	0	215	252	252	79	0	0	0	0	0	0	126	253	247	176	9	0	0	8	78	245	253	129	0	0	0	0	0	0	0	16	232	252	176	0	0	36	201	252	252	169	11	0	0	0	0	0	0	0	22	252	252	30	22	119	197	241	253	252	251	77	0	0	0	0	0	0	0	0	16	231	252	253	252	252	252	226	227	252	231	0	0	0	0	0	0	0	0	0	55	235	253	217	138	42	24	192	252	143	0	0	0	0	0	0	0	0	0	0	0	0	62	255	253	109	0	0	0	0	0	0	0	0	0	0	0	71	253	252	21	0	0	0	0	0	0	0	0	0	0	0	253	252	21	0	0	0	0	0	0	0	0	0	0	71	253	252	21	0	0	0	0	0	0	0	0	0	0	106	253	252	21	0	0	0	0	0	0	0	0	0	0	45	255	253	21	0	0	0	0	0	0	0	0	0	0	0	0	0	218	252	56	0	0	0	0	0	0	0	0	0	0	0	96	252	189	42	0	0	14	184	252	170	11	14	147	252	42

	0	95	96	97	98	99	100	101	122	123	124	125	126	127	128	129	130	131	149	150	151	152	153	154	155	156	157	158	159	160	161	176	177	178	179	180	181	182	183	184	185	186	188	189	203	204	205	206	207	208	209	210	211	212	213	214	215	216	217	218	231	232	233	234	235	236	237	238	239	240	241	242	243	244	245	246	259	260	261	262	263	264	265	266	267	268	269	270	271	272	273	274	289	290	291	292	293	294	295	296	297	298	299	300	301	302	316	317	318	319	320	321	322	323	324	325	326	327	328	329	330	331	344	345	346	347	348	349	350	351	352	353	354	355	356	357	358	359	371	372	373	374	375	376	377	378	379	380	381	382	383	384	385	386	387	399	400	401	402	403	404	405	406	407	408	409	410	411	412	413	414	415	427	428	429	430	431	432	433	434	435	436	437	438	439	440	441	442	443	456	457	458	459	460	461	462	463	464	465	466	467	468	469	470	471	484	485	486	487	488	489	490	491	492	493	494	495	496	497	498	512	513	514	515	516	517	518	519	520	521	522	523	524	525	540	541	542	543	544	545	546	547	548	549	550	551	552	553	554	555	556	557	558	559	568	569	570	571	572	573	574	575	576	577	578	579	580	581	582	583	584	585	586	587	596	597	598	599	600	601	602	603	604	605	606	607	608	609	610	611	612	613	614	615	625	626	627	628	629	630	631	632	633	634	635	636	637	655	656	657	658	659	660	661	662	663	683	684	685	686	687	689	690	711	712	713	714	715	739	740	741	742
0	7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	84	185	159	151	60	36	0	0	0	0	0	0	0	0	0	0	222	254	254	254	254	241	198	198	198	198	198	198	198	198	170	52	67	114	72	114	163	227	254	225	254	254	254	250	229	254	254	140	0	0	0	17	66	14	67	67	67	59	21	236	254	106	0	0	0	0	0	0	0	0	0	0	0	83	253	209	18	0	0	0	0	0	0	0	0	0	0	0	22	233	255	83	0	0	0	0	0	0	0	0	0	0	0	0	0	129	254	238	44	0	0	0	0	0	0	0	0	0	0	0	0	59	249	254	62	0	0	0	0	0	0	0	0	0	0	0	0	0	133	254	187	5	0	0	0	0	0	0	0	0	0	0	0	9	205	248	58	0	0	0	0	0	0	0	0	0	0	0	0	126	254	182	0	0	0	0	0	0	0	0	0	0	0	75	251	240	57	0	0	0	0	0	0	0	0	0	19	221	254	166	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	203	254	219	35	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	38	254	254	77	0	0	0	0	0	0	0	0	0	0	0	0	0	0	31	224	254	115	1	0	0	0	0	0	0	133	254	254	52	0	0	0	0	61	242	254	254	52	0	0	121	254	254	219	40	121	254	207	18
1	2	116	125	171	255	255	150	93	169	253	253	253	253	253	253	218	30	0	169	253	253	253	213	142	176	253	253	122	0	0	0	52	250	253	210	32	12	0	6	206	253	140	0	0	0	77	251	210	25	0	0	0	122	248	253	65	0	0	0	0	0	0	31	18	0	0	0	0	209	253	253	65	0	0	0	0	0	0	0	0	0	0	0	117	247	253	198	10	0	0	0	0	0	0	0	0	76	247	253	231	63	0	0	0	0	0	0	0	0	0	0	128	253	253	144	0	0	0	0	0	0	0	0	0	0	0	176	246	253	159	12	0	0	0	0	0	0	0	0	0	0	0	25	234	253	233	35	0	0	0	0	0	0	0	0	0	0	0	0	198	253	253	141	0	0	0	0	0	0	0	0	0	0	0	0	78	248	253	189	12	0	0	0	0	0	0	0	0	0	0	19	200	253	253	141	0	0	0	0	0	0	0	0	0	0	0	134	253	253	173	12	0	0	0	0	0	0	0	0	0	0	248	253	253	25	0	0	0	0	0	0	0	0	0	0	248	253	253	43	20	20	20	20	5	0	5	20	20	37	150	150	150	147	10	0	248	253	253	253	253	253	253	253	168	143	166	253	253	253	253	253	253	253	123	0	174	253	253	253	253	253	253	253	253	253	253	253	249	247	247	169	117	117	57	0	118	123	123	123	166	253	253	253	155	123	123	41	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	38	254	109	0	0	0	0	0	0	0	0	87	252	82	0	0	0	0	0	0	0	0	0	0	0	135	241	0	0	0	0	0	0	0	0	0	0	0	45	244	150	0	0	0	0	0	0	0	0	0	0	0	0	0	84	254	63	0	0	0	0	0	0	0	0	0	0	0	0	0	202	223	11	0	0	0	0	0	0	0	0	0	0	32	254	216	0	0	0	0	0	0	0	0	0	0	0	0	95	254	195	0	0	0	0	0	0	0	0	0	0	0	0	0	140	254	77	0	0	0	0	0	0	0	0	0	0	0	0	0	57	237	205	8	0	0	0	0	0	0	0	0	0	0	0	0	0	124	255	165	0	0	0	0	0	0	0	0	0	0	0	0	0	0	171	254	81	0	0	0	0	0	0	0	0	0	0	0	0	24	232	215	0	0	0	0	0	0	0	0	0	0	0	0	0	120	254	159	0	0	0	0	0	0	0	0	0	0	0	0	151	254	142	0	0	0	0	0	0	0	0	0	0	0	228	254	66	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	61	251	254	66	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	141	254	205	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	10	215	254	121	0	0	0	0	0	0	0	5	198	176	10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	0	11	150	253	202	31	0	0	0	0	0	0	37	251	251	253	107	0	0	0	0	0	0	0	0	21	197	251	251	253	107	0	0	0	0	0	0	0	110	190	251	251	251	253	169	109	62	0	0	0	0	0	0	0	253	251	251	251	251	253	251	251	220	51	0	0	0	0	0	182	255	253	253	253	253	234	222	253	253	253	0	0	63	221	253	251	251	251	147	77	62	128	251	251	105	0	32	231	251	253	251	220	137	10	0	0	31	230	251	243	113	5	37	251	251	253	188	20	0	0	0	0	0	109	251	253	251	35	0	37	251	251	201	30	0	0	0	0	0	0	31	200	253	251	35	0	37	253	253	0	0	0	0	0	0	0	0	32	202	255	253	164	0	140	251	251	0	0	0	0	0	0	0	0	109	251	253	251	35	217	251	251	0	0	0	0	0	0	21	63	231	251	253	230	30	217	251	251	0	0	0	0	0	0	144	251	251	251	221	61	217	251	251	0	0	0	0	0	182	221	251	251	251	180	218	253	253	73	73	228	253	253	255	253	253	253	253	0	0	0	0	0	0	0	113	251	251	253	251	251	251	251	253	251	251	251	147	0	0	0	0	0	0	0	31	230	251	253	251	251	251	251	253	230	189	35	10	0	0	0	0	0	0	0	62	142	253	251	251	251	251	253	107	0	0	0	0	72	174	251	173	71	72	30	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	50	224	0	0	0	0	0	0	0	70	29	0	0	0	121	231	0	0	0	0	0	0	148	168	0	0	0	4	195	231	0	0	0	0	0	0	0	96	210	11	0	0	0	69	252	134	0	0	0	0	0	0	0	114	252	21	0	0	45	236	217	12	0	0	0	0	0	0	0	192	252	21	168	247	53	0	0	0	0	0	0	0	18	255	253	21	84	242	211	0	0	0	0	0	0	0	0	141	253	189	5	0	169	252	106	0	0	0	0	0	0	0	32	232	250	66	0	0	15	225	252	0	0	0	0	0	0	0	0	134	252	211	0	0	0	22	252	164	0	0	0	0	0	0	0	0	169	252	167	0	0	0	9	204	209	18	0	0	0	0	0	0	22	253	253	107	0	0	0	169	252	199	85	85	85	85	129	164	195	252	252	106	0	0	0	41	170	245	252	252	252	252	232	231	251	252	252	9	0	0	0	0	49	84	84	84	84	0	0	161	252	252	0	0	0	0	0	0	0	0	0	0	0	127	252	252	45	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	128	253	253	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	127	252	252	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	135	252	244	0	0	0	0	0	0	0	0	232	236	111	0	0	0	0	0	179	66	0	0	0	0	0	0	0	0	0

	0	128	129	130	131	132	153	154	155	156	157	158	159	160	161	162	163	164	173	174	177	178	179	180	181	182	183	184	185	186	187	188	189	190	191	192	201	202	204	205	206	207	208	209	210	211	212	213	214	215	216	217	218	219	229	230	232	233	234	235	236	237	238	239	240	241	242	243	244	245	246	257	258	261	262	263	264	265	266	267	268	269	270	271	272	273	274	284	285	286	290	291	292	293	294	295	296	297	298	299	300	301	302	303	312	313	314	317	318	319	320	321	322	323	324	325	326	327	328	329	330	331	340	341	342	344	345	346	347	348	349	350	351	352	353	354	355	356	357	358	359	368	369	370	371	372	373	374	375	377	378	379	380	381	382	383	384	385	386	387	396	397	398	399	400	401	402	403	404	405	406	407	408	409	410	411	412	413	414	415	424	425	426	427	428	429	430	431	432	433	434	435	436	437	438	439	440	441	442	443	453	454	455	456	457	458	459	460	461	462	463	464	465	466	467	468	469	470	471	483	484	485	487	488	489	490	491	492	493	494	495	496	497	498	511	512	513	515	516	517	518	519	520	521	522	523	524	525	539	540	541	543	544	545	546	547	548	549	550	551	552	567	568	569	570	571	572	573	574	575	576	577	578	579	580	595	596	597	598	599	600	601	602	603	604	605	606	607	608	623	624	625	626	627	628	629	630	631	632	633	634	635	636	649	650	651	652	653	654	655	656	657	658	659	660	661	662	663	664	677	678	679	680	681	682	683	684	687	688	689	690	691	692	715	716	717	718	719	744	745	746	747
0	5	0	0	0	0	0	3	18	18	18	126	136	175	26	166	255	247	127	0	0	30	36	94	154	170	253	253	253	253	253	225	172	253	242	195	64	0	0	49	238	253	253	253	253	253	253	253	253	251	93	82	82	56	39	0	0	18	219	253	253	253	253	253	198	182	247	241	0	0	0	0	0	0	80	156	107	253	253	205	11	0	43	154	0	0	0	0	0	0	0	14	1	154	253	90	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	139	253	190	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	11	190	253	70	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	35	241	225	160	108	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	81	240	253	253	119	25	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	45	186	253	253	150	27	0	0	0	0	0	0	0	0	0	0	0	0	0	0	16	93	252	253	187	0	0	0	0	0	0	0	0	0	0	0	0	0	249	253	249	64	0	0	0	0	0	0	0	0	46	130	183	253	253	207	2	0	0	0	0	0	39	148	229	253	253	253	250	182	0	0	0	0	24	114	221	253	253	253	253	201	78	0	0	0	23	66	213	253	253	253	253	198	81	2	0	0	18	171	219	253	253	253	253	195	80	9	0	0	0	0	55	172	226	253	253	253	253	244	133	11	0	0	0	0	0	0	136	253	253	253	212	135	132	16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1	0	51	159	253	159	50	0	0	48	238	252	252	252	237	0	0	0	0	0	0	0	0	0	0	0	54	227	253	252	239	233	252	57	6	0	0	0	0	0	0	0	0	10	60	224	252	253	252	202	84	252	253	122	0	0	0	0	0	0	0	163	252	252	252	253	252	252	96	189	253	167	0	0	0	0	51	238	253	253	190	114	253	228	47	79	255	168	0	0	0	48	238	252	252	179	12	75	121	21	0	0	253	243	50	0	0	0	38	165	253	233	208	84	0	0	0	0	0	0	253	252	165	0	0	0	7	178	252	240	71	19	28	0	0	0	0	0	0	253	252	195	0	0	0	0	57	252	252	63	0	0	0	0	0	0	0	0	253	252	195	0	0	0	0	198	253	190	0	0	0	0	0	0	0	0	0	0	255	253	196	0	0	0	76	246	252	112	0	0	0	0	0	0	0	0	0	0	253	252	148	0	0	85	252	230	25	0	0	0	0	0	0	0	0	7	135	253	186	12	85	252	223	0	0	0	0	0	0	0	7	131	252	225	71	85	252	145	0	0	0	0	0	0	48	165	252	173	0	86	253	225	0	0	0	0	0	114	238	253	162	0	85	252	249	146	48	29	85	178	225	253	223	167	56	0	85	252	252	252	229	215	252	252	252	196	130	0	0	0	28	199	252	252	253	252	252	233	145	0	0	0	0	0	0	0	0	25	128	252	253	252	141	37	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	4	0	0	0	0	0	0	0	0	0	0	0	0	0	67	232	39	0	62	81	0	0	0	0	0	0	0	0	0	0	0	0	120	180	39	0	126	163	0	0	0	0	0	0	0	0	0	0	0	0	2	153	210	40	220	163	0	0	0	0	0	0	0	0	0	0	0	0	27	254	162	222	163	0	0	0	0	0	0	0	0	0	0	0	183	254	125	46	245	163	0	0	0	0	0	0	0	0	0	0	198	254	56	0	120	254	163	0	0	0	0	0	0	0	0	0	0	23	231	254	29	0	159	254	120	0	0	0	0	0	0	0	0	0	0	0	163	254	216	16	0	159	254	67	0	0	0	0	0	0	0	0	14	86	178	248	254	91	0	0	159	254	85	0	0	0	47	49	116	144	150	241	243	234	179	241	252	40	0	0	150	253	237	207	207	207	253	254	250	240	198	143	91	28	5	233	250	0	0	0	119	177	177	177	177	177	98	56	0	0	0	0	0	102	254	220	0	0	0	0	0	0	0	0	0	0	0	0	0	169	254	137	0	0	0	0	0	0	0	0	0	0	0	0	169	254	57	0	0	0	0	0	0	0	0	0	0	0	169	254	57	0	0	0	0	0	0	0	0	0	0	0	169	255	94	0	0	0	0	0	0	0	0	0	0	0	169	254	96	0	0	0	0	0	0	0	0	0	0	0	169	254	153	0	0	0	0	0	0	0	0	0	0	0	0	0	169	255	153	0	0	0	0	0	0	0	0	0	0	0	96	254	153	0	0	0	0	0	0	0	0	0
3	1	0	0	0	0	0	0	0	0	0	0	0	124	253	255	63	0	0	0	0	0	0	0	0	0	0	0	0	0	96	244	251	253	62	0	0	0	0	0	0	0	0	0	0	0	0	0	0	127	251	251	253	62	0	0	0	0	0	0	0	0	0	0	0	0	68	236	251	211	31	8	0	0	0	0	0	0	0	0	0	60	228	251	251	94	0	0	0	0	0	0	0	0	0	0	0	155	253	253	189	0	0	0	0	0	0	0	0	0	0	0	0	0	20	253	251	235	66	0	0	0	0	0	0	0	0	0	0	0	0	0	32	205	253	251	126	0	0	0	0	0	0	0	0	0	0	0	0	0	0	104	251	253	184	15	0	0	0	0	0	0	0	0	0	0	0	0	0	0	80	240	251	193	23	0	0	0	0	0	0	0	0	0	0	0	0	0	0	32	253	253	253	159	0	0	0	0	0	0	0	0	0	0	0	0	0	0	151	251	251	251	39	0	0	0	0	0	0	0	0	0	0	48	221	251	251	172	0	0	0	0	0	0	0	0	0	0	234	251	251	196	12	0	0	0	0	0	0	0	0	0	253	251	251	89	0	0	0	0	0	0	0	0	0	159	255	253	253	31	0	0	0	0	0	0	0	0	48	228	253	247	140	8	0	0	0	0	0	0	0	0	64	251	253	220	0	0	0	0	0	0	0	0	0	0	0	0	64	251	253	220	0	0	0	0	0	0	0	0	0	0	0	0	24	193	253	220	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	55	148	210	253	253	113	87	148	55	0	0	0	0	0	0	0	0	87	232	252	253	189	210	252	252	253	168	0	0	0	0	4	57	242	252	190	65	5	12	182	252	253	116	0	0	0	0	96	252	252	183	14	0	0	92	252	252	225	21	0	0	0	0	0	132	253	252	146	14	0	0	0	215	252	252	79	0	0	0	0	0	0	126	253	247	176	9	0	0	8	78	245	253	129	0	0	0	0	0	0	0	16	232	252	176	0	0	36	201	252	252	169	11	0	0	0	0	0	0	0	22	252	252	30	22	119	197	241	253	252	251	77	0	0	0	0	0	0	0	0	16	231	252	253	252	252	252	226	227	252	231	0	0	0	0	0	0	0	0	0	55	235	253	217	138	42	24	192	252	143	0	0	0	0	0	0	0	0	0	0	0	0	62	255	253	109	0	0	0	0	0	0	0	0	0	0	0	71	253	252	21	0	0	0	0	0	0	0	0	0	0	0	253	252	21	0	0	0	0	0	0	0	0	0	0	71	253	252	21	0	0	0	0	0	0	0	0	0	0	106	253	252	21	0	0	0	0	0	0	0	0	0	0	45	255	253	21	0	0	0	0	0	0	0	0	0	0	0	0	0	218	252	56	0	0	0	0	0	0	0	0	0	0	0	96	252	189	42	0	0	14	184	252	170	11	14	147	252	42

	0	95	96	97	98	99	100	101	122	123	124	125	126	127	128	129	130	131	149	150	151	152	153	154	155	156	157	158	159	160	161	176	177	178	179	180	181	182	183	184	185	186	188	189	203	204	205	206	207	208	209	210	211	212	213	214	215	216	217	218	231	232	233	234	235	236	237	238	239	240	241	242	243	244	245	246	259	260	261	262	263	264	265	266	267	268	269	270	271	272	273	274	289	290	291	292	293	294	295	296	297	298	299	300	301	302	316	317	318	319	320	321	322	323	324	325	326	327	328	329	330	331	344	345	346	347	348	349	350	351	352	353	354	355	356	357	358	359	371	372	373	374	375	376	377	378	379	380	381	382	383	384	385	386	387	399	400	401	402	403	404	405	406	407	408	409	410	411	412	413	414	415	427	428	429	430	431	432	433	434	435	436	437	438	439	440	441	442	443	456	457	458	459	460	461	462	463	464	465	466	467	468	469	470	471	484	485	486	487	488	489	490	491	492	493	494	495	496	497	498	512	513	514	515	516	517	518	519	520	521	522	523	524	525	540	541	542	543	544	545	546	547	548	549	550	551	552	553	554	555	556	557	558	559	568	569	570	571	572	573	574	575	576	577	578	579	580	581	582	583	584	585	586	587	596	597	598	599	600	601	602	603	604	605	606	607	608	609	610	611	612	613	614	615	625	626	627	628	629	630	631	632	633	634	635	636	637	655	656	657	658	659	660	661	662	663	683	684	685	686	687	689	690	711	712	713	714	715	739	740	741	742
0	7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	84	185	159	151	60	36	0	0	0	0	0	0	0	0	0	0	222	254	254	254	254	241	198	198	198	198	198	198	198	198	170	52	67	114	72	114	163	227	254	225	254	254	254	250	229	254	254	140	0	0	0	17	66	14	67	67	67	59	21	236	254	106	0	0	0	0	0	0	0	0	0	0	0	83	253	209	18	0	0	0	0	0	0	0	0	0	0	0	22	233	255	83	0	0	0	0	0	0	0	0	0	0	0	0	0	129	254	238	44	0	0	0	0	0	0	0	0	0	0	0	0	59	249	254	62	0	0	0	0	0	0	0	0	0	0	0	0	0	133	254	187	5	0	0	0	0	0	0	0	0	0	0	0	9	205	248	58	0	0	0	0	0	0	0	0	0	0	0	0	126	254	182	0	0	0	0	0	0	0	0	0	0	0	75	251	240	57	0	0	0	0	0	0	0	0	0	19	221	254	166	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	203	254	219	35	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	38	254	254	77	0	0	0	0	0	0	0	0	0	0	0	0	0	0	31	224	254	115	1	0	0	0	0	0	0	133	254	254	52	0	0	0	0	61	242	254	254	52	0	0	121	254	254	219	40	121	254	207	18
1	2	116	125	171	255	255	150	93	169	253	253	253	253	253	253	218	30	0	169	253	253	253	213	142	176	253	253	122	0	0	0	52	250	253	210	32	12	0	6	206	253	140	0	0	0	77	251	210	25	0	0	0	122	248	253	65	0	0	0	0	0	0	31	18	0	0	0	0	209	253	253	65	0	0	0	0	0	0	0	0	0	0	0	117	247	253	198	10	0	0	0	0	0	0	0	0	76	247	253	231	63	0	0	0	0	0	0	0	0	0	0	128	253	253	144	0	0	0	0	0	0	0	0	0	0	0	176	246	253	159	12	0	0	0	0	0	0	0	0	0	0	0	25	234	253	233	35	0	0	0	0	0	0	0	0	0	0	0	0	198	253	253	141	0	0	0	0	0	0	0	0	0	0	0	0	78	248	253	189	12	0	0	0	0	0	0	0	0	0	0	19	200	253	253	141	0	0	0	0	0	0	0	0	0	0	0	134	253	253	173	12	0	0	0	0	0	0	0	0	0	0	248	253	253	25	0	0	0	0	0	0	0	0	0	0	248	253	253	43	20	20	20	20	5	0	5	20	20	37	150	150	150	147	10	0	248	253	253	253	253	253	253	253	168	143	166	253	253	253	253	253	253	253	123	0	174	253	253	253	253	253	253	253	253	253	253	253	249	247	247	169	117	117	57	0	118	123	123	123	166	253	253	253	155	123	123	41	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	38	254	109	0	0	0	0	0	0	0	0	87	252	82	0	0	0	0	0	0	0	0	0	0	0	135	241	0	0	0	0	0	0	0	0	0	0	0	45	244	150	0	0	0	0	0	0	0	0	0	0	0	0	0	84	254	63	0	0	0	0	0	0	0	0	0	0	0	0	0	202	223	11	0	0	0	0	0	0	0	0	0	0	32	254	216	0	0	0	0	0	0	0	0	0	0	0	0	95	254	195	0	0	0	0	0	0	0	0	0	0	0	0	0	140	254	77	0	0	0	0	0	0	0	0	0	0	0	0	0	57	237	205	8	0	0	0	0	0	0	0	0	0	0	0	0	0	124	255	165	0	0	0	0	0	0	0	0	0	0	0	0	0	0	171	254	81	0	0	0	0	0	0	0	0	0	0	0	0	24	232	215	0	0	0	0	0	0	0	0	0	0	0	0	0	120	254	159	0	0	0	0	0	0	0	0	0	0	0	0	151	254	142	0	0	0	0	0	0	0	0	0	0	0	228	254	66	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	61	251	254	66	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	141	254	205	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	10	215	254	121	0	0	0	0	0	0	0	5	198	176	10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	0	11	150	253	202	31	0	0	0	0	0	0	37	251	251	253	107	0	0	0	0	0	0	0	0	21	197	251	251	253	107	0	0	0	0	0	0	0	110	190	251	251	251	253	169	109	62	0	0	0	0	0	0	0	253	251	251	251	251	253	251	251	220	51	0	0	0	0	0	182	255	253	253	253	253	234	222	253	253	253	0	0	63	221	253	251	251	251	147	77	62	128	251	251	105	0	32	231	251	253	251	220	137	10	0	0	31	230	251	243	113	5	37	251	251	253	188	20	0	0	0	0	0	109	251	253	251	35	0	37	251	251	201	30	0	0	0	0	0	0	31	200	253	251	35	0	37	253	253	0	0	0	0	0	0	0	0	32	202	255	253	164	0	140	251	251	0	0	0	0	0	0	0	0	109	251	253	251	35	217	251	251	0	0	0	0	0	0	21	63	231	251	253	230	30	217	251	251	0	0	0	0	0	0	144	251	251	251	221	61	217	251	251	0	0	0	0	0	182	221	251	251	251	180	218	253	253	73	73	228	253	253	255	253	253	253	253	0	0	0	0	0	0	0	113	251	251	253	251	251	251	251	253	251	251	251	147	0	0	0	0	0	0	0	31	230	251	253	251	251	251	251	253	230	189	35	10	0	0	0	0	0	0	0	62	142	253	251	251	251	251	253	107	0	0	0	0	72	174	251	173	71	72	30	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	50	224	0	0	0	0	0	0	0	70	29	0	0	0	121	231	0	0	0	0	0	0	148	168	0	0	0	4	195	231	0	0	0	0	0	0	0	96	210	11	0	0	0	69	252	134	0	0	0	0	0	0	0	114	252	21	0	0	45	236	217	12	0	0	0	0	0	0	0	192	252	21	168	247	53	0	0	0	0	0	0	0	18	255	253	21	84	242	211	0	0	0	0	0	0	0	0	141	253	189	5	0	169	252	106	0	0	0	0	0	0	0	32	232	250	66	0	0	15	225	252	0	0	0	0	0	0	0	0	134	252	211	0	0	0	22	252	164	0	0	0	0	0	0	0	0	169	252	167	0	0	0	9	204	209	18	0	0	0	0	0	0	22	253	253	107	0	0	0	169	252	199	85	85	85	85	129	164	195	252	252	106	0	0	0	41	170	245	252	252	252	252	232	231	251	252	252	9	0	0	0	0	49	84	84	84	84	0	0	161	252	252	0	0	0	0	0	0	0	0	0	0	0	127	252	252	45	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	128	253	253	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	127	252	252	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	135	252	244	0	0	0	0	0	0	0	0	232	236	111	0	0	0	0	0	179	66	0	0	0	0	0	0	0	0	0

	0	128	129	130	131	132	153	154	155	156	157	158	159	160	161	162	163	164	173	174	177	178	179	180	181	182	183	184	185	186	187	188	189	190	191	192	201	202	204	205	206	207	208	209	210	211	212	213	214	215	216	217	218	219	229	230	232	233	234	235	236	237	238	239	240	241	242	243	244	245	246	257	258	261	262	263	264	265	266	267	268	269	270	271	272	273	274	284	285	286	290	291	292	293	294	295	296	297	298	299	300	301	302	303	312	313	314	317	318	319	320	321	322	323	324	325	326	327	328	329	330	331	340	341	342	344	345	346	347	348	349	350	351	352	353	354	355	356	357	358	359	368	369	370	371	372	373	374	375	377	378	379	380	381	382	383	384	385	386	387	396	397	398	399	400	401	402	403	404	405	406	407	408	409	410	411	412	413	414	415	424	425	426	427	428	429	430	431	432	433	434	435	436	437	438	439	440	441	442	443	453	454	455	456	457	458	459	460	461	462	463	464	465	466	467	468	469	470	471	483	484	485	487	488	489	490	491	492	493	494	495	496	497	498	511	512	513	515	516	517	518	519	520	521	522	523	524	525	539	540	541	543	544	545	546	547	548	549	550	551	552	567	568	569	570	571	572	573	574	575	576	577	578	579	580	595	596	597	598	599	600	601	602	603	604	605	606	607	608	623	624	625	626	627	628	629	630	631	632	633	634	635	636	649	650	651	652	653	654	655	656	657	658	659	660	661	662	663	664	677	678	679	680	681	682	683	684	687	688	689	690	691	692	715	716	717	718	719	744	745	746	747
0	5	0	0	0	0	0	3	18	18	18	126	136	175	26	166	255	247	127	0	0	30	36	94	154	170	253	253	253	253	253	225	172	253	242	195	64	0	0	49	238	253	253	253	253	253	253	253	253	251	93	82	82	56	39	0	0	18	219	253	253	253	253	253	198	182	247	241	0	0	0	0	0	0	80	156	107	253	253	205	11	0	43	154	0	0	0	0	0	0	0	14	1	154	253	90	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	139	253	190	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	11	190	253	70	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	35	241	225	160	108	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	81	240	253	253	119	25	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	45	186	253	253	150	27	0	0	0	0	0	0	0	0	0	0	0	0	0	0	16	93	252	253	187	0	0	0	0	0	0	0	0	0	0	0	0	0	249	253	249	64	0	0	0	0	0	0	0	0	46	130	183	253	253	207	2	0	0	0	0	0	39	148	229	253	253	253	250	182	0	0	0	0	24	114	221	253	253	253	253	201	78	0	0	0	23	66	213	253	253	253	253	198	81	2	0	0	18	171	219	253	253	253	253	195	80	9	0	0	0	0	55	172	226	253	253	253	253	244	133	11	0	0	0	0	0	0	136	253	253	253	212	135	132	16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1	0	51	159	253	159	50	0	0	48	238	252	252	252	237	0	0	0	0	0	0	0	0	0	0	0	54	227	253	252	239	233	252	57	6	0	0	0	0	0	0	0	0	10	60	224	252	253	252	202	84	252	253	122	0	0	0	0	0	0	0	163	252	252	252	253	252	252	96	189	253	167	0	0	0	0	51	238	253	253	190	114	253	228	47	79	255	168	0	0	0	48	238	252	252	179	12	75	121	21	0	0	253	243	50	0	0	0	38	165	253	233	208	84	0	0	0	0	0	0	253	252	165	0	0	0	7	178	252	240	71	19	28	0	0	0	0	0	0	253	252	195	0	0	0	0	57	252	252	63	0	0	0	0	0	0	0	0	253	252	195	0	0	0	0	198	253	190	0	0	0	0	0	0	0	0	0	0	255	253	196	0	0	0	76	246	252	112	0	0	0	0	0	0	0	0	0	0	253	252	148	0	0	85	252	230	25	0	0	0	0	0	0	0	0	7	135	253	186	12	85	252	223	0	0	0	0	0	0	0	7	131	252	225	71	85	252	145	0	0	0	0	0	0	48	165	252	173	0	86	253	225	0	0	0	0	0	114	238	253	162	0	85	252	249	146	48	29	85	178	225	253	223	167	56	0	85	252	252	252	229	215	252	252	252	196	130	0	0	0	28	199	252	252	253	252	252	233	145	0	0	0	0	0	0	0	0	25	128	252	253	252	141	37	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	4	0	0	0	0	0	0	0	0	0	0	0	0	0	67	232	39	0	62	81	0	0	0	0	0	0	0	0	0	0	0	0	120	180	39	0	126	163	0	0	0	0	0	0	0	0	0	0	0	0	2	153	210	40	220	163	0	0	0	0	0	0	0	0	0	0	0	0	27	254	162	222	163	0	0	0	0	0	0	0	0	0	0	0	183	254	125	46	245	163	0	0	0	0	0	0	0	0	0	0	198	254	56	0	120	254	163	0	0	0	0	0	0	0	0	0	0	23	231	254	29	0	159	254	120	0	0	0	0	0	0	0	0	0	0	0	163	254	216	16	0	159	254	67	0	0	0	0	0	0	0	0	14	86	178	248	254	91	0	0	159	254	85	0	0	0	47	49	116	144	150	241	243	234	179	241	252	40	0	0	150	253	237	207	207	207	253	254	250	240	198	143	91	28	5	233	250	0	0	0	119	177	177	177	177	177	98	56	0	0	0	0	0	102	254	220	0	0	0	0	0	0	0	0	0	0	0	0	0	169	254	137	0	0	0	0	0	0	0	0	0	0	0	0	169	254	57	0	0	0	0	0	0	0	0	0	0	0	169	254	57	0	0	0	0	0	0	0	0	0	0	0	169	255	94	0	0	0	0	0	0	0	0	0	0	0	169	254	96	0	0	0	0	0	0	0	0	0	0	0	169	254	153	0	0	0	0	0	0	0	0	0	0	0	0	0	169	255	153	0	0	0	0	0	0	0	0	0	0	0	96	254	153	0	0	0	0	0	0	0	0	0
3	1	0	0	0	0	0	0	0	0	0	0	0	124	253	255	63	0	0	0	0	0	0	0	0	0	0	0	0	0	96	244	251	253	62	0	0	0	0	0	0	0	0	0	0	0	0	0	0	127	251	251	253	62	0	0	0	0	0	0	0	0	0	0	0	0	68	236	251	211	31	8	0	0	0	0	0	0	0	0	0	60	228	251	251	94	0	0	0	0	0	0	0	0	0	0	0	155	253	253	189	0	0	0	0	0	0	0	0	0	0	0	0	0	20	253	251	235	66	0	0	0	0	0	0	0	0	0	0	0	0	0	32	205	253	251	126	0	0	0	0	0	0	0	0	0	0	0	0	0	0	104	251	253	184	15	0	0	0	0	0	0	0	0	0	0	0	0	0	0	80	240	251	193	23	0	0	0	0	0	0	0	0	0	0	0	0	0	0	32	253	253	253	159	0	0	0	0	0	0	0	0	0	0	0	0	0	0	151	251	251	251	39	0	0	0	0	0	0	0	0	0	0	48	221	251	251	172	0	0	0	0	0	0	0	0	0	0	234	251	251	196	12	0	0	0	0	0	0	0	0	0	253	251	251	89	0	0	0	0	0	0	0	0	0	159	255	253	253	31	0	0	0	0	0	0	0	0	48	228	253	247	140	8	0	0	0	0	0	0	0	0	64	251	253	220	0	0	0	0	0	0	0	0	0	0	0	0	64	251	253	220	0	0	0	0	0	0	0	0	0	0	0	0	24	193	253	220	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	55	148	210	253	253	113	87	148	55	0	0	0	0	0	0	0	0	87	232	252	253	189	210	252	252	253	168	0	0	0	0	4	57	242	252	190	65	5	12	182	252	253	116	0	0	0	0	96	252	252	183	14	0	0	92	252	252	225	21	0	0	0	0	0	132	253	252	146	14	0	0	0	215	252	252	79	0	0	0	0	0	0	126	253	247	176	9	0	0	8	78	245	253	129	0	0	0	0	0	0	0	16	232	252	176	0	0	36	201	252	252	169	11	0	0	0	0	0	0	0	22	252	252	30	22	119	197	241	253	252	251	77	0	0	0	0	0	0	0	0	16	231	252	253	252	252	252	226	227	252	231	0	0	0	0	0	0	0	0	0	55	235	253	217	138	42	24	192	252	143	0	0	0	0	0	0	0	0	0	0	0	0	62	255	253	109	0	0	0	0	0	0	0	0	0	0	0	71	253	252	21	0	0	0	0	0	0	0	0	0	0	0	253	252	21	0	0	0	0	0	0	0	0	0	0	71	253	252	21	0	0	0	0	0	0	0	0	0	0	106	253	252	21	0	0	0	0	0	0	0	0	0	0	45	255	253	21	0	0	0	0	0	0	0	0	0	0	0	0	0	218	252	56	0	0	0	0	0	0	0	0	0	0	0	96	252	189	42	0	0	14	184	252	170	11	14	147	252	42

	0	95	96	97	98	99	100	101	122	123	124	125	126	127	128	129	130	131	149	150	151	152	153	154	155	156	157	158	159	160	161	176	177	178	179	180	181	182	183	184	185	186	188	189	203	204	205	206	207	208	209	210	211	212	213	214	215	216	217	218	231	232	233	234	235	236	237	238	239	240	241	242	243	244	245	246	259	260	261	262	263	264	265	266	267	268	269	270	271	272	273	274	289	290	291	292	293	294	295	296	297	298	299	300	301	302	316	317	318	319	320	321	322	323	324	325	326	327	328	329	330	331	344	345	346	347	348	349	350	351	352	353	354	355	356	357	358	359	371	372	373	374	375	376	377	378	379	380	381	382	383	384	385	386	387	399	400	401	402	403	404	405	406	407	408	409	410	411	412	413	414	415	427	428	429	430	431	432	433	434	435	436	437	438	439	440	441	442	443	456	457	458	459	460	461	462	463	464	465	466	467	468	469	470	471	484	485	486	487	488	489	490	491	492	493	494	495	496	497	498	512	513	514	515	516	517	518	519	520	521	522	523	524	525	540	541	542	543	544	545	546	547	548	549	550	551	552	553	554	555	556	557	558	559	568	569	570	571	572	573	574	575	576	577	578	579	580	581	582	583	584	585	586	587	596	597	598	599	600	601	602	603	604	605	606	607	608	609	610	611	612	613	614	615	625	626	627	628	629	630	631	632	633	634	635	636	637	655	656	657	658	659	660	661	662	663	683	684	685	686	687	689	690	711	712	713	714	715	739	740	741	742
0	7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	84	185	159	151	60	36	0	0	0	0	0	0	0	0	0	0	222	254	254	254	254	241	198	198	198	198	198	198	198	198	170	52	67	114	72	114	163	227	254	225	254	254	254	250	229	254	254	140	0	0	0	17	66	14	67	67	67	59	21	236	254	106	0	0	0	0	0	0	0	0	0	0	0	83	253	209	18	0	0	0	0	0	0	0	0	0	0	0	22	233	255	83	0	0	0	0	0	0	0	0	0	0	0	0	0	129	254	238	44	0	0	0	0	0	0	0	0	0	0	0	0	59	249	254	62	0	0	0	0	0	0	0	0	0	0	0	0	0	133	254	187	5	0	0	0	0	0	0	0	0	0	0	0	9	205	248	58	0	0	0	0	0	0	0	0	0	0	0	0	126	254	182	0	0	0	0	0	0	0	0	0	0	0	75	251	240	57	0	0	0	0	0	0	0	0	0	19	221	254	166	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	203	254	219	35	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	38	254	254	77	0	0	0	0	0	0	0	0	0	0	0	0	0	0	31	224	254	115	1	0	0	0	0	0	0	133	254	254	52	0	0	0	0	61	242	254	254	52	0	0	121	254	254	219	40	121	254	207	18
1	2	116	125	171	255	255	150	93	169	253	253	253	253	253	253	218	30	0	169	253	253	253	213	142	176	253	253	122	0	0	0	52	250	253	210	32	12	0	6	206	253	140	0	0	0	77	251	210	25	0	0	0	122	248	253	65	0	0	0	0	0	0	31	18	0	0	0	0	209	253	253	65	0	0	0	0	0	0	0	0	0	0	0	117	247	253	198	10	0	0	0	0	0	0	0	0	76	247	253	231	63	0	0	0	0	0	0	0	0	0	0	128	253	253	144	0	0	0	0	0	0	0	0	0	0	0	176	246	253	159	12	0	0	0	0	0	0	0	0	0	0	0	25	234	253	233	35	0	0	0	0	0	0	0	0	0	0	0	0	198	253	253	141	0	0	0	0	0	0	0	0	0	0	0	0	78	248	253	189	12	0	0	0	0	0	0	0	0	0	0	19	200	253	253	141	0	0	0	0	0	0	0	0	0	0	0	134	253	253	173	12	0	0	0	0	0	0	0	0	0	0	248	253	253	25	0	0	0	0	0	0	0	0	0	0	248	253	253	43	20	20	20	20	5	0	5	20	20	37	150	150	150	147	10	0	248	253	253	253	253	253	253	253	168	143	166	253	253	253	253	253	253	253	123	0	174	253	253	253	253	253	253	253	253	253	253	253	249	247	247	169	117	117	57	0	118	123	123	123	166	253	253	253	155	123	123	41	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	38	254	109	0	0	0	0	0	0	0	0	87	252	82	0	0	0	0	0	0	0	0	0	0	0	135	241	0	0	0	0	0	0	0	0	0	0	0	45	244	150	0	0	0	0	0	0	0	0	0	0	0	0	0	84	254	63	0	0	0	0	0	0	0	0	0	0	0	0	0	202	223	11	0	0	0	0	0	0	0	0	0	0	32	254	216	0	0	0	0	0	0	0	0	0	0	0	0	95	254	195	0	0	0	0	0	0	0	0	0	0	0	0	0	140	254	77	0	0	0	0	0	0	0	0	0	0	0	0	0	57	237	205	8	0	0	0	0	0	0	0	0	0	0	0	0	0	124	255	165	0	0	0	0	0	0	0	0	0	0	0	0	0	0	171	254	81	0	0	0	0	0	0	0	0	0	0	0	0	24	232	215	0	0	0	0	0	0	0	0	0	0	0	0	0	120	254	159	0	0	0	0	0	0	0	0	0	0	0	0	151	254	142	0	0	0	0	0	0	0	0	0	0	0	228	254	66	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	61	251	254	66	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	141	254	205	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	10	215	254	121	0	0	0	0	0	0	0	5	198	176	10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	0	11	150	253	202	31	0	0	0	0	0	0	37	251	251	253	107	0	0	0	0	0	0	0	0	21	197	251	251	253	107	0	0	0	0	0	0	0	110	190	251	251	251	253	169	109	62	0	0	0	0	0	0	0	253	251	251	251	251	253	251	251	220	51	0	0	0	0	0	182	255	253	253	253	253	234	222	253	253	253	0	0	63	221	253	251	251	251	147	77	62	128	251	251	105	0	32	231	251	253	251	220	137	10	0	0	31	230	251	243	113	5	37	251	251	253	188	20	0	0	0	0	0	109	251	253	251	35	0	37	251	251	201	30	0	0	0	0	0	0	31	200	253	251	35	0	37	253	253	0	0	0	0	0	0	0	0	32	202	255	253	164	0	140	251	251	0	0	0	0	0	0	0	0	109	251	253	251	35	217	251	251	0	0	0	0	0	0	21	63	231	251	253	230	30	217	251	251	0	0	0	0	0	0	144	251	251	251	221	61	217	251	251	0	0	0	0	0	182	221	251	251	251	180	218	253	253	73	73	228	253	253	255	253	253	253	253	0	0	0	0	0	0	0	113	251	251	253	251	251	251	251	253	251	251	251	147	0	0	0	0	0	0	0	31	230	251	253	251	251	251	251	253	230	189	35	10	0	0	0	0	0	0	0	62	142	253	251	251	251	251	253	107	0	0	0	0	72	174	251	173	71	72	30	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	50	224	0	0	0	0	0	0	0	70	29	0	0	0	121	231	0	0	0	0	0	0	148	168	0	0	0	4	195	231	0	0	0	0	0	0	0	96	210	11	0	0	0	69	252	134	0	0	0	0	0	0	0	114	252	21	0	0	45	236	217	12	0	0	0	0	0	0	0	192	252	21	168	247	53	0	0	0	0	0	0	0	18	255	253	21	84	242	211	0	0	0	0	0	0	0	0	141	253	189	5	0	169	252	106	0	0	0	0	0	0	0	32	232	250	66	0	0	15	225	252	0	0	0	0	0	0	0	0	134	252	211	0	0	0	22	252	164	0	0	0	0	0	0	0	0	169	252	167	0	0	0	9	204	209	18	0	0	0	0	0	0	22	253	253	107	0	0	0	169	252	199	85	85	85	85	129	164	195	252	252	106	0	0	0	41	170	245	252	252	252	252	232	231	251	252	252	9	0	0	0	0	49	84	84	84	84	0	0	161	252	252	0	0	0	0	0	0	0	0	0	0	0	127	252	252	45	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	128	253	253	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	127	252	252	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	135	252	244	0	0	0	0	0	0	0	0	232	236	111	0	0	0	0	0	179	66	0	0	0	0	0	0	0	0	0