code stringlengths 3 6.57k |
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set_ResourceOwnerAccount(self,ResourceOwnerAccount) |
self.add_query_param('ResourceOwnerAccount',ResourceOwnerAccount) |
get_OwnerAccount(self) |
self.get_query_params() |
get('OwnerAccount') |
set_OwnerAccount(self,OwnerAccount) |
self.add_query_param('OwnerAccount',OwnerAccount) |
get_OwnerId(self) |
self.get_query_params() |
get('OwnerId') |
set_OwnerId(self,OwnerId) |
self.add_query_param('OwnerId',OwnerId) |
pd.read_csv("Chinese_Names_Corpus_Gender（120W）.txt", header=2) |
list(name) |
list(itertools.chain(*chars) |
collections.Counter(chars_flatten) |
pd.DataFrame(freq.items() |
freq.sort_values(by="freq", ascending=False) |
law (?) |
np.arange(freq.shape[0]) |
plt.plot(char_rank, char_freq) |
plt.plot(np.log(1.0 + char_rank) |
np.log(char_freq) |
list(freq["char"].values[:dict_size]) |
set(dict) |
list(filter(lambda item: set(item[1]) |
issubset(dict_set) |
enumerate(names) |
np.where(dat["sex"] == "男", 0, 1) |
dat.sample(frac=0.8, random_state=123) |
dat.drop(train.index) |
dat.sample(n=10000, random_state=123) |
dat.sample(n=1000, random_state=321) |
char2index(char) |
dict.index(char) |
name2index(name) |
char2index(char) |
name2tensor(name) |
torch.zeros(len(name) |
enumerate(name) |
char2index(char) |
char2index("李") |
name2index("李兴") |
name2tensor("李兴") |
RNN(nn.Module) |
__init__(self, input_size, hidden_size) |
super(RNN, self) |
__init__() |
nn.Linear(input_size + hidden_size, hidden_size) |
nn.Linear(hidden_size, 1) |
forward(self, input, hidden) |
torch.cat((input, hidden) |
torch.tanh(self.i2h(combined) |
torch.sigmoid(self.h2o(hidden) |
init_hidden(self) |
torch.zeros(1, self.hidden_size) |
RNN(dict_size, n_hidden) |
name2tensor("李兴") |
rnn.init_hidden() |
rnn(input[0], hidden) |
np.random.seed(123) |
torch.random.manual_seed(123) |
RNN(dict_size, n_hidden) |
torch.optim.Adam(rnn.parameters() |
np.arange(n) |
time.time() |
range(nepoch) |
np.random.shuffle(train_ind) |
range(0, n, bs) |
range(mb_size) |
name2tensor(name) |
rnn.init_hidden() |
range(input.shape[0]) |
rnn(input[s], hidden) |
torch.log(output) |
torch.log(1.0 - output) |
opt.zero_grad() |
loss.backward() |
opt.step() |
losses.append(loss.item() |
print(f"epoch {k}, batch {j // bs}, loss = {loss.item() |
time.time() |
print(t2 - t1) |
plt.plot(losses) |
np.zeros(ntest) |
rnn.eval() |
range(ntest) |
name2tensor(test["dict"].values[i]) |
rnn.init_hidden() |
torch.no_grad() |
range(input.shape[0]) |
rnn(input[s], hidden) |
output.item() |
print(f"processed {i}") |
np.mean(true_label * np.log(pred) |
np.log(1.0 - pred) |
print(loss) |
astype(int) |
print(np.mean(pred_label == true_label) |
np.random.seed(123) |
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