Wu, Bin(Ben) benwu232
benwu232
/ gist:216aadccb935717543311e75641d2624
Created
September 3, 2024 03:20
mixin example for dart
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| abstract class Animal { | |
| void breath() { | |
| print('breathing'); | |
| } | |
| } | |
| mixin Swim { | |
| void swim() { | |
| print('swimming'); | |
| } |
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| import ast | |
| import sys | |
| #test_string = '{"Nikhil" : 1, "Akshat" : 2, "Akash" : 3}' | |
| in_str = "dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 -le -de 13 -le a=1 c=2 dict2=-ds f=3 h=i -de j=k -de" | |
| # dict1=-ds1 a=1 c=2 dict2=-ds2 f=3 h=i -de2 j=k -de2 | |
| # dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 seq2=-ls 9 -le -le -de 13 -le a=1 c=2 dict3=-ds f=3 h=i -de j=k -de | |
| def parse_dict(str_in): |
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| import ast | |
| import sys | |
| #test_string = '{"Nikhil" : 1, "Akshat" : 2, "Akash" : 3}' | |
| in_str = "dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 -le -de 13 -le a=1 c=2 dict2=-ds f=3 h=i -de j=k -de" | |
| # dict1=-ds1 a=1 c=2 dict2=-ds2 f=3 h=i -de2 j=k -de2 | |
| # dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 seq2=-ls 9 -le -le -de 13 -le a=1 c=2 dict3=-ds f=3 h=i -de j=k -de | |
| def parse_dict(str_in): |
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| import ast | |
| import sys | |
| #test_string = '{"Nikhil" : 1, "Akshat" : 2, "Akash" : 3}' | |
| in_str = "dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 -le -de 13 -le a=1 c=2 dict2=-ds f=3 h=i -de j=k -de" | |
| # dict1=-ds1 a=1 c=2 dict2=-ds2 f=3 h=i -de2 j=k -de2 | |
| # dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 seq2=-ls 9 -le -le -de 13 -le a=1 c=2 dict3=-ds f=3 h=i -de j=k -de | |
| def parse_dict(str_in): |
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| import ast | |
| import sys | |
| #test_string = '{"Nikhil" : 1, "Akshat" : 2, "Akash" : 3}' | |
| in_str = "dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 -le -de 13 -le a=1 c=2 dict2=-ds f=3 h=i -de j=k -de" | |
| # dict1=-ds1 a=1 c=2 dict2=-ds2 f=3 h=i -de2 j=k -de2 | |
| # dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 seq2=-ls 9 -le -le -de 13 -le a=1 c=2 dict3=-ds f=3 h=i -de j=k -de | |
| def parse_dict(str_in): |
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| import ast | |
| import sys | |
| #test_string = '{"Nikhil" : 1, "Akshat" : 2, "Akash" : 3}' | |
| in_str = "dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 -le -de 13 -le a=1 c=2 dict2=-ds f=3 h=i -de j=k -de" | |
| # dict1=-ds1 a=1 c=2 dict2=-ds2 f=3 h=i -de2 j=k -de2 | |
| # dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 seq2=-ls 9 -le -le -de 13 -le a=1 c=2 dict3=-ds f=3 h=i -de j=k -de | |
| def parse_dict(str_in): |
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| import ast | |
| import sys | |
| #test_string = '{"Nikhil" : 1, "Akshat" : 2, "Akash" : 3}' | |
| in_str = "dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 -le -de 13 -le a=1 c=2 dict2=-ds f=3 h=i -de j=k -de" | |
| # dict1=-ds1 a=1 c=2 dict2=-ds2 f=3 h=i -de2 j=k -de2 | |
| # dict1=-ds ar=-ls 10 11 12 dict2=-ds x=1 y=2 seq=-ls 7 8 seq2=-ls 9 -le -le -de 13 -le a=1 c=2 dict3=-ds f=3 h=i -de j=k -de | |
| def parse_dict(str_in): |
benwu232
/ gist:1fbf1cd6b637810f5d57902fa6d4ef1b
Created
August 25, 2017 06:50
weight matrix loss for pytorch
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| def one_hot(size, index): | |
| """ Creates a matrix of one hot vectors. | |
| ``` | |
| import torch | |
| import torch_extras | |
| setattr(torch, 'one_hot', torch_extras.one_hot) | |
| size = (3, 3) | |
| index = torch.LongTensor([2, 0, 1]).view(-1, 1) | |
| torch.one_hot(size, index) | |
| # [[0, 0, 1], [1, 0, 0], [0, 1, 0]] |
benwu232
/ tf_restore.py
Created
February 8, 2017 07:11
Show how to restore model, parameters, inputs and outputs
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| import tensorflow as tf | |
| tf.GraphKeys.USEFUL = 'useful' | |
| saver = tf.train.import_meta_graph("./model_ex1.meta") | |
| sess = tf.Session() | |
| saver.restore(sess, "./model_ex1") | |
| var_list = tf.get_collection(tf.GraphKeys.USEFUL) | |
| v1 = var_list[0] |
benwu232
/ tf_save.py
Created
February 8, 2017 07:09
Describe how to use tensorflow to save model, parameters, inputs and outputs
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| import tensorflow as tf | |
| tf.GraphKeys.USEFUL = 'useful' | |
| v1 = tf.placeholder(tf.float32, name="v1") | |
| v2 = tf.placeholder(tf.float32, name="v2") | |
| v3 = tf.mul(v1, v2) | |
| vx = tf.Variable(10.0, name="vx") | |
| v4 = tf.add(v3, vx, name="v4") |
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