def main():
global solution
# 创建Data类实例对象
data = Data('src_data/data.txt')
# 调用data对象的solution_judge方法，来判断该同余方程组是否有解
if (data.error_flag):
if data.solution_judge():
print("该同余式组有解")
# 如果有解，则分两种方式求解
# 先调用crt_judge方法，判断是否可以用中国剩余定理求解
if data.crt_judge():
print("该同余式组可以用中国剩余定理")
sol = "解为: x=%d(mod %d)" % Crt(data).crt_compute()
print(sol)
# 将答案写入文件
with open("src_data/data.txt", "a") as f:
f.write("\n" + sol)
return sol
# 如果crt_judge方法返回0，则调用nst方法，用一般求解方式进行求解
else:
print("该同余式组不可以用中国剩余定理，以一般方法求解")
sol = "解为: x=%d(mod %d)" % Nst(data).nst_compute()
print(sol)
# 将答案写入文件
with open("src_data/data.txt", "a") as f:
f.write("\n" + sol)
return sol
else:
solution = "该同余式组无解"
print("该同余式组无解")
return solution
else:
return data.solution
def main():
equation = []
if not os.path.exists(dir):
os.mkdir(dir)
os.chdir(dir)
num = int(input("请输入一共有多少个同余方程："))
with open('da ta.txt', 'w') as f:
for i in range(num):
equation.append(input("请输入第%d个方程：" % i) + "\n")
f.write(equation[i])
else:
print("目录已存在")
os.chdir(dir)
data = Data('./data.txt')
if data.solution_judge():
print("该同余式组有解")
if data.crt_judge():
print("该同余式组可以用中国剩余定理")
print("解为: x=%d(mod %d)" % Crt(data).crt_compute())
else:
print("该同余式组不可以用中国剩余定理，以一般方法求解")
print("解为: x=%d(mod %d)" % Nst(data).nst_compute())
else:
print("该同余式组无解")
return False
def __init__(self, hparams, data_dir):
self.hparams = hparams
self.data_dir = data_dir
#logger
self._logger = logging.getLogger(__name__)
#data_process
self.data_process = Data(self.hparams, self.data_dir)
(self.char_inputs, self.char_lengths), (self.inputs, self.labels, self.lengths) = \
self.data_process.load_data()
#char, id
self.char2id = self.data_process.char2id
self.id2char = self.data_process.id2char
# word, id
self.word2id = self.data_process.word2id # dict()
self.id2word = self.data_process.id2word # vocabulary
# label, id
self.label2id = self.data_process.label2id
self.id2label = self.data_process.id2label
# pre-trained word2vec
with np.load(os.path.join(self.hparams.glove_dir, "glove.6B.300d.trimmed.npz")) as pretrained_data:
self.word_embeddings = pretrained_data["embeddings"]
print(np.shape(self.word_embeddings))
def nst_compute(self):
for i in range(self.num_equation):
a, b = prime_Decomposition(self.mod[i])
self.base2.extend(a)
self.exponent.extend(b)
for j in range(len(a)):
self.remainder2.append(self.remainder[i])
self.num_equation2 += 1
# print("********************")
# print(self.base2)
# print(self.exponent)
# print(self.num_equation)
# print(self.num_equation2)
# print("********************")
for i in range(self.num_equation2):
for j in range(i+1, self.num_equation2):
if self.base2[i] == self.base2[j]:
flag = int(self.exponent[i] > self.exponent[j])
c = [self.exponent[j], self.exponent[i]][self.exponent[j] > self.exponent[i]]
b = [self.exponent[i], self.exponent[j]][self.exponent[j] > self.exponent[i]]
if abs(self.remainder2[i] - self.remainder2[j]) % pow(self.base2[i], c) == 0:
if flag:
self.remainder3.append(self.remainder2[i])
else:
self.remainder3.append(self.remainder2[j])
self.base3.append(self.base2[i])
self.exponent3.append(b)
self.num_equation3 += 1
else:
print("矛盾，该同余式组无解")
for index, value in enumerate(self.base2):
if self.base2.count(value) == 1:
self.remainder3.append(self.remainder2[index])
self.base3.append(self.base2[index])
self.exponent3.append(self.exponent[index])
self.num_equation3 += 1
# print("&&&&&&&&&&&&&&&&&&&&&&")
# print(self.base3)
# print(self.exponent3)
# print(self.num_equation3)
# print(self.remainder3)
# print("&&&&&&&&&&&&&&&&&&&&&&")
with open("transformed_data.txt", 'w') as f:
for i in range(self.num_equation3):
f.write("x=%d(mod%d)\n" % (self.remainder3[i], pow(self.base3[i], self.exponent3[i])))
transformed_data = Data("./transformed_data.txt")
if transformed_data.crt_judge():
print("该同余式组可以用中国剩余定理")
tcrt = Crt(transformed_data)
return tcrt.crt_compute()
else:
print("以一般方法 递归 求解")
return Nst(transformed_data).nst_compute()
def _pre_process(self):
self.model_file = os.path.join(self.model_dir, 'model.ckpt')
self.meta_file = os.path.join(self.model_dir, 'model.ckpt.meta')
var_file = os.path.join(self.model_dir, 'var.pkl')
with open(var_file, 'rb') as f:
self.var, self.config = pickle.load(f)
basic_config = config.basic_config()
basic_config.__dict__.update(self.config)
self.config = basic_config
vocab_file = './data/vocab.txt'
self.data_tools = Data(vocab_file, None, basic_config, logging)
self.tokenizer = Tokenizer(logging)
def __init__(self):
config = Config()
self.emb = UtteranceEmbed(config.word2vec_filename)
train_dataset = Data(config.train_filename, config.test_filename).train_set
random.shuffle(train_dataset)
self.train_dataset = train_dataset[:361]
self.test_dataset = train_dataset[361:]
self.cate_mapping_dict = joblib.load(config.cate_mapping_dict)
self.bow_embed = BowEmbed(self.train_dataset, self.train_dataset, 200)
nb_hidden = 128
# obs_size = self.emb.dim + self.bow_embed.get_vocab_size()
obs_size = self.emb.dim
self.memory_net = Memory_net(obs_size, nb_hidden)
def main(config):
data = Data(config.train_filename, config.dev_filename,
config.test_filename)
train_data = data.train_set
dev_data = data.dev_set
test_data = data.test_set
# load word2vec
utter_embed = UtteranceEmbed(config.word2vec_filename)
input_size = (utter_embed.get_vector_size() * 2) # concat size
num_neurons = [7500, 7500, 5000, 2500]
model = Dnn(input_size, num_neurons, 3, utter_embed, config)
model.build()
model.train(train_data, dev_data, test_data)
def predict_score():
data = Data()
test_features = feature_extraction(data, "test")
submission = pd.DataFrame.from_dict({'id': data.test['id']})
for i in range(len(data.classes)):
print("Processing " + data.classes[i])
lr_model_pkl = open(
'../model/logistic_regression_%s.pkl' % data.classes[i], 'rb')
lr_model = pickle.load(lr_model_pkl)
print(
"Loaded Logistic Regression Model for class %s :: " %
data.classes[i], lr_model)
submission[data.classes[i]] = lr_model.predict_proba(test_features)[:,
1]
print(submission.head(5))
print("Saving output")
submission.to_csv('../data/output.csv', index=False)
print("Output saved")
def create_and_save():
data = Data()
train_features = feature_extraction(data, "train")
scores = []
# print (train_features.shape)
# kbest = SelectKBest(chi2, k=1000)
for i in range(len(data.classes)):
print("Processing " + data.classes[i])
train_target = data.train[data.classes[i]]
# x_feature = kbest.fit_transform(train_features, train_target)
# print (x_feature)
classifier = LogisticRegression(solver='sag')
cv_score = np.mean(
cross_val_score(classifier,
train_features,
train_target,
cv=3,
scoring='roc_auc'))
# cv_score = np.mean(cross_val_score(classifier, x_feature, train_target, cv=3, scoring='roc_auc'))
scores.append(cv_score)
print('CV score for class {} is {}'.format(data.classes[i], cv_score))
# Calculate ROC_AUC
roc_auc(train_features, np.array(train_target), data.classes[i])
print("Creating model for class " + data.classes[i])
classifier.fit(train_features, train_target)
# classifier.fit(x_feature, train_target)
print("Saving model logistic_regression_%s" % data.classes[i])
lr_pkl_filename = '../model/logistic_regression_%s.pkl' % data.classes[
i]
lr_model_pkl = open(lr_pkl_filename, 'wb')
pickle.dump(classifier, lr_model_pkl)
lr_model_pkl.close()
print("Model saved")
print('Total CV score is {}'.format(np.mean(scores)))
print("Successfully created and saved all models!")
total_loss / data_len, total_acc / data_len))
np.save(result_src, total_y_pre)
return y, total_y_pre
if __name__ == "__main__":
# 模型路径
model_save_src = "data/model/2_layer_lstm_model"
num_category = 9
# 向量化后的数据集
x_src = "data/vectorized_data/test/x.npy"
y_src = "data/vectorized_data/test/y.npy"
result_src = "data/results/rnn_pre.npy"
vocab_src = "data/middle_result/vocab.npy"
data = Data()
vocab, _ = data.load_vocab(vocab_src)
# 模型
config = TRNNConfig()
config.vocab_size = len(vocab)
model = TextRNN(config)
# 测试
print("Begin Testing")
start_time = time.time()
y, y_pre = test(x_src, y_src, result_src)
print("the time is {}".format(get_time_dif(start_time)))
# 评估
precision_score, recall_score, f1_val, accuracy = evaluate(y, y_pre)
def train():
epochs = 50
length = 10
n_units = 128
n_features = 6
batch_size = 64
data = Data(batch_size)
num_batches = data.num_batches()
xplaceholder = tf.placeholder(tf.float32, shape=[None, length, n_features])
yplaceholder = tf.placeholder(tf.float32, shape=[None, 1])
midPrice_means = tf.placeholder(tf.float32, shape=[None, 1])
midPrice_stddevs = tf.placeholder(tf.float32, shape=[None, 1])
#origin midPrice
origin_midPrice = yplaceholder * midPrice_stddevs + midPrice_means
pred = lstm_model(xplaceholder, n_units)
#pred midPrice
pred_midPrice = pred * midPrice_stddevs + midPrice_means
loss = tf.losses.mean_squared_error(labels=yplaceholder, predictions=pred)
tf.summary.scalar('loss', loss)
accuracy = tf.sqrt(
tf.losses.mean_squared_error(origin_midPrice, pred_midPrice))
tf.summary.scalar('accuracy', accuracy)
step = tf.Variable(0)
learning_rate = 1e-4
tf.summary.scalar('learning rate', learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(
loss, global_step=step)
merged = tf.summary.merge_all()
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
train_writer = tf.summary.FileWriter('log', graph=sess.graph)
saver = tf.train.Saver(max_to_keep=3)
step_val = None
for epoch in range(epochs):
data.reset_batch()
total_loss = 0.0
total_acc = 0.0
for i in range(num_batches):
batch_inputs, batch_labels, batch_means, batch_stddevs = data.next_batch(
)
feed_dict = {
xplaceholder: batch_inputs,
yplaceholder: batch_labels,
midPrice_means: batch_means,
midPrice_stddevs: batch_stddevs
}
_, loss_val, acc_val, step_val, summary = sess.run(
[optimizer, loss, accuracy, step, merged],
feed_dict=feed_dict)
total_acc += acc_val
total_loss += loss_val
train_writer.add_summary(summary, global_step=step_val)
print 'Epoch', epoch, 'train_loss', total_loss / num_batches, 'train_acc', total_acc / num_batches
'''
dev_inputs, dev_labels = data.get_dev_data()
feed_dict = {xplaceholder: dev_inputs, yplaceholder: dev_labels}
acc_val, loss_val = sess.run([accuracy, loss], feed_dict = feed_dict)
print 'dev_loss', loss_val, 'dev_acc', acc_val
'''
outfile = open('outputs10.csv', 'w')
outfile.write('midprice\n')
test_inputs_list, test_means_list, test_stddevs_list = data.get_test_data(
)
for i in range(data.test_num_half_day):
test_means = []
test_stddevs = []
test_inputs = test_inputs_list[i]
mean = test_means_list[i][0]
stddev = test_stddevs_list[i][0]
for j in range(len(test_inputs)):
test_means.append(mean)
test_stddevs.append(stddev)
test_inputs = np.asarray(test_inputs)
test_means = np.asarray(test_means).reshape([-1, 1])
test_stddevs = np.asarray(test_stddevs).reshape([-1, 1])
feed_dict = {
xplaceholder: test_inputs,
midPrice_means: test_means,
midPrice_stddevs: test_stddevs
}
pred_val = sess.run(pred_midPrice, feed_dict=feed_dict)
pred_val = np.asarray(pred_val)
#print pred_val.shape
for i in range(len(pred_val)):
outfile.write(str(pred_val[i][0]) + '\n')
outfile.close()
if total_batch - last_improved > require_improvement:
# 验证集正确率长期不提升，提前结束训练
print("No optimization for a long time, auto-stopping...")
flag = True
break # 跳出循环
if flag: # 同上
break
print("the best acc on validation is {}".format(best_acc_val))
if __name__ == '__main__':
train_dir = "data/vectorized_data/train"
val_dir = "data/vectorized_data/validation"
vocab_dir = "data/file_dict/train/vocab.npy"
save_dir = 'data/model2'
data_process = Data()
config = CharCNNConfig()
if not os.path.exists(vocab_dir):
data_process.build_vocab(train_dir, vocab_dir)
words, word_to_id = data_process.load_vocab(vocab_dir)
config.vocab_size = len(words)
model = CharCNN(config)
train()
class TextClassifier:
def __init__(self, hparams, data_dir):
self.hparams = hparams
self.data_dir = data_dir
#logger
self._logger = logging.getLogger(__name__)
#data_process
self.data_process = Data(self.hparams, self.data_dir)
(self.char_inputs, self.char_lengths), (self.inputs, self.labels, self.lengths) = \
self.data_process.load_data()
#char, id
self.char2id = self.data_process.char2id
self.id2char = self.data_process.id2char
# word, id
self.word2id = self.data_process.word2id # dict()
self.id2word = self.data_process.id2word # vocabulary
# label, id
self.label2id = self.data_process.label2id
self.id2label = self.data_process.id2label
# pre-trained word2vec
with np.load(os.path.join(self.hparams.glove_dir, "glove.6B.300d.trimmed.npz")) as pretrained_data:
self.word_embeddings = pretrained_data["embeddings"]
print(np.shape(self.word_embeddings))
def _inference(self, inputs: tf.Tensor, lengths: tf.Tensor, char_inputs: tf.Tensor, char_lengths: tf.Tensor):
print("Building graph for model: Text Classifier")
# Number of possible output cateIories.
output_dim = len(self.id2label) # output_dim -> 2
char_vocab_size = len(self.id2char)
char_embeddings = tf.get_variable(
name="char_embeddings",
shape=[char_vocab_size, self.hparams.char_embedding_dim],
initializer=tf.initializers.variance_scaling(
scale=2.0, mode="fan_in", distribution="uniform"
)
)
char_embedded = tf.nn.embedding_lookup(char_embeddings, char_inputs)
# default shape [batch_size, sentence_time, word_time, char_embedding(50)]
# reshape [batch_size * sentence_time, word_time, char_embeddings(50)]
char_embedded = tf.reshape(char_embedded, [-1, tf.shape(char_inputs)[-1], self.hparams.char_embedding_dim])
# reshape [batch_size * word_time]
char_lengths = tf.reshape(char_lengths, [-1])
with tf.variable_scope("char-bi-RNN"):
char_rnn_cell_forward = rnn.GRUCell(self.hparams.char_embedding_dim)
char_rnn_cell_backward = rnn.GRUCell(self.hparams.char_embedding_dim)
if self.hparams.dropout_keep_prob < 1.0:
char_rnn_cell_forward = rnn.DropoutWrapper(char_rnn_cell_forward,
output_keep_prob=self._dropout_keep_prob_ph)
char_rnn_cell_backward = rnn.DropoutWrapper(char_rnn_cell_backward,
output_keep_prob=self._dropout_keep_prob_ph)
_, (char_output_fw_states, char_output_bw_states) = \
tf.nn.bidirectional_dynamic_rnn(
char_rnn_cell_forward, char_rnn_cell_backward,
inputs=char_embedded,
sequence_length=char_lengths,
dtype=tf.float32
)
char_hiddens = tf.concat([char_output_fw_states, char_output_bw_states], axis=-1)
char_hiddens = tf.reshape(char_hiddens,
[tf.shape(char_inputs)[0], tf.shape(char_inputs)[1], self.hparams.char_embedding_dim*2])
word_embeddings = tf.Variable(
self.word_embeddings,
name="word_embeddings",
dtype=tf.float32,
trainable=True
)
## shape = [batch_size, time, embed_dim]
word_embedded = tf.nn.embedding_lookup(word_embeddings, inputs)
char_word_inputs = tf.concat([word_embedded, char_hiddens], axis=-1)
with tf.variable_scope("bi-RNN"):
# Build RNN layers
rnn_cell_forward = rnn.GRUCell(self.hparams.rnn_hidden_dim)
rnn_cell_backward = rnn.GRUCell(self.hparams.rnn_hidden_dim)
# Apply dropout to RNN
if self.hparams.dropout_keep_prob < 1.0:
rnn_cell_forward = tf.contrib.rnn.DropoutWrapper(rnn_cell_forward,
output_keep_prob=self._dropout_keep_prob_ph)
rnn_cell_backward = tf.contrib.rnn.DropoutWrapper(rnn_cell_backward,
output_keep_prob=self._dropout_keep_prob_ph)
_, (states_fw_final, states_bw_final) = \
tf.nn.bidirectional_dynamic_rnn(
rnn_cell_forward, rnn_cell_backward,
inputs=char_word_inputs,
sequence_length=lengths,
dtype=tf.float32
)
# shape = [batch_size, rnn_hidden_dim * 2]
final_hiddens = tf.concat([states_fw_final, states_bw_final], axis=-1)
with tf.variable_scope("layer_out"):
layer_out = tf.layers.dense(
inputs=final_hiddens,
units=output_dim,
activation=None,
kernel_initializer=tf.initializers.variance_scaling(
scale=2.0, mode="fan_in", distribution="normal"
)
)
return layer_out
def make_placeholder(self):
self.inputs_ph = tf.placeholder(tf.int32, shape=[None, None], name="train_input_ph")
self.labels_ph = tf.placeholder(tf.int32, shape=[None], name="train_label_ph")
self.lengths_ph = tf.placeholder(tf.int32, shape=[None], name="train_lengths_ph")
#[batch_size, word_time, char_time]
self.char_inputs_ph = tf.placeholder(tf.int32, shape=[None, None, None], name="char_input_ph")
self.char_lengths_ph = tf.placeholder(tf.int32, shape=[None, None], name="char_lengths_ph")
self._dropout_keep_prob_ph = tf.placeholder(tf.float32, shape=[], name="dropout_keep_prob")
def make_feed_dict(self, batch_data):
feed_dict = {}
batch_inputs, batch_labels, batch_lengths, batch_char_inputs, batch_char_lengths = batch_data
# word-level
feed_dict[self.inputs_ph] = batch_inputs
feed_dict[self.labels_ph] = batch_labels
feed_dict[self.lengths_ph] = batch_lengths
# char-level
feed_dict[self.char_inputs_ph] = batch_char_inputs
feed_dict[self.char_lengths_ph] = batch_char_lengths
feed_dict[self._dropout_keep_prob_ph] = self.hparams.dropout_keep_prob
return feed_dict
def build_graph(self):
self.global_step = tf.Variable(0, name='global_step', trainable=False)
# logits
with tf.variable_scope("inference", reuse=False):
logits = self._inference(self.inputs_ph, self.lengths_ph, self.char_inputs_ph, self.char_lengths_ph)
with tf.name_scope("cross_entropy"):
loss_op = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=self.labels_ph,
name="cross_entropy")
self.loss_op = tf.reduce_mean(loss_op, name='cross_entropy_mean')
self.train_op = tf.train.AdamOptimizer().minimize(loss_op, global_step=self.global_step)
eval = tf.nn.in_top_k(logits, self.labels_ph, 1)
correct_count = tf.reduce_sum(tf.cast(eval, tf.int32))
with tf.name_scope("accuracy"):
self.accuracy = tf.divide(correct_count, tf.shape(self.labels_ph)[0])
def train(self):
sess = tf.Session()
with sess.as_default():
global_step = tf.Variable(0, name='global_step', trainable=False)
# build placeholder
self.make_placeholder()
# build train graph
self.build_graph()
# checkpoint file saver
saver = tf.train.Saver()
# get data
inputs_id, labels_id, chars_id = \
self.data_process.data_id(self.inputs, self.labels, self.char_inputs)
total_batch = int(len(inputs_id) / self.hparams.batch_size) + 1
tf.global_variables_initializer().run()
for epochs_completed in range(self.hparams.num_epochs):
for iter in range(total_batch):
batch_data = self.data_process.get_batch_data(inputs_id, labels_id, self.lengths,
chars_id, self.char_lengths,
iter, self.hparams.batch_size)
accuracy_val, loss_val, global_step_val, _ = sess.run(
[self.accuracy, self.loss_op, self.global_step, self.train_op],
feed_dict=self.make_feed_dict(batch_data)
)
if global_step_val % 10 == 0:
self._logger.info("[Step %d] loss: %.4f, accuracy: %.2f%%" % (
global_step_val, loss_val, accuracy_val * 100))
self._logger.info("End of epoch %d." % (epochs_completed + 1))
save_path = saver.save(sess, os.path.join(self.hparams.root_dir,
"saves_%s/model.ckpt" % (self.hparams.model)),
global_step=global_step_val)
self._logger.info("Model saved at: %s" % save_path)
from data_process import Data
if __name__ == '__main__':
target_data = Data()
path = ".././source/origin_source/C1_data.xlsx"
# 首先还原出我原先版本的结构
# 最少要读入5行数据，否则会报错
# 首先处理500的数据量级
target_data.classfing_all_data()
'''
nrows = 5000
# 数据读入并设置基本参数
target_data.reset()
target_data.store_data_with_can_matrix_for_single_canid(path, nrows)
target_data.set_data_length()
# 进行分类
target_data.initial_classfy_data()
target_data.process_classfy_data()
# 贪心算法求最优解
target_data.greedy_find_solution()
# 显示
target_data.show_results()
# target_data.check_sensor_or_counter()
target_data.show_counter()
target_data.show_sensor()
def nst_compute(self):
# 首先对同余式方程组的模进行质数分解（调用prime_Decomposition函数）
# 质数分解获得模的标准分解式，然后构成了第二套等价的方程组
for i in range(self.num_equation):
a, b = prime_Decomposition(self.mod[i])
self.base2.extend(a)
self.exponent.extend(b)
for j in range(len(a)):
self.remainder2.append(self.remainder[i])
self.num_equation2 += 1
# 双重循环遍历因数分解后的同余方程式组
# 这一步将使同余式方程组符合中国剩余定理
for i in range(self.num_equation2):
for j in range(i + 1, self.num_equation2):
if self.base2[i] == self.base2[j]:
# flag为了标记第i项和第j项指数到底是谁比较大
flag = int(self.exponent[i] > self.exponent[j])
# c为指数较小项
# b为较大指数项
c = [self.exponent[j], self.exponent[i]
][self.exponent[j] >= self.exponent[i]]
b = [self.exponent[i],
self.exponent[j]][self.exponent[j] > self.exponent[i]]
# 判别条件：(base^c)|余数差
if abs(self.remainder2[i] - self.remainder2[j]) % pow(
self.base2[i], c) == 0:
# 按条件扩充余数列表
if flag:
self.remainder3.append(self.remainder2[i])
else:
self.remainder3.append(self.remainder2[j])
# 按条件扩充底数，指数，并且方程个数增加一
self.base3.append(self.base2[i])
self.exponent3.append(b)
self.num_equation3 += 1
else:
print("矛盾，该同余式组无解")
exit()
# 将同余方程式组中落单的方程添加到最终方程组中
# 使用了enumerate函数，使用列表的count方法，
# 若某项只出现一次，用该索引值获取该项值，并添加到相应列表中
for index, value in enumerate(self.base2):
if self.base2.count(value) == 1:
self.remainder3.append(self.remainder2[index])
self.base3.append(self.base2[index])
self.exponent3.append(self.exponent[index])
self.num_equation3 += 1
# 创建新文件，构造最终方程式组并将该方程式组放入txt文件中，
# 这样就可以用该路径创建新的数据对象
# 到此为止，历尽千辛万苦构造的新同余式方程组就可能满足中国剩余定理了，如果满足，只要调用crt类即可
with open("src_data/transformed_data.txt", 'w') as f:
for i in range(self.num_equation3):
f.write("x=%d(mod%d)\n" %
(self.remainder3[i],
pow(self.base3[i], self.exponent3[i])))
# 构造Data对象
transformed_data = Data("src_data/transformed_data.txt")
# 调用crt_judge方法，若返回1，则说明经过一轮转化已经满足中国剩余定理
if transformed_data.crt_judge():
tcrt = Crt(transformed_data)
return tcrt.crt_compute()
# 如果crt_judge方法返回0，则说明经过一轮转化并不转化成满足中国剩余定理形式，但已经更接近中国剩余定理条件
# 此时则需要进行递归运算，将此时的transformed_data再次传入nst，进行新的一轮三部转化
else:
print("以一般方法 递归 求解")
return Nst(transformed_data).nst_compute()