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2025 AI4S TEEN Cup Challenge Math——Parameter Solving of Recursive Sequence_baseline
陈乐天 Letian Chen
更新于 2024-12-05
推荐镜像 :ai4s-cup:0.3.1
推荐机型 :c2_m4_cpu
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数据集
rain(v1)
[3]
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
import os
# Define the regression model (participants need to design and fill it in themselves)
# Function for parameter solving (participants need to design and fill it in themselves)
def pqregression(data):
p_value = 1
q_value = 1
return p_value, q_value
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if __name__ == '__main__':
#-----Read the training set, the address of the training set has been set, and the following section does not need to be modified-------#
train_path = "/bohr/train-btk3/v1/a_seq_train.csv"
data_train = pd.read_csv(train_path)
#----Read the testing set, “DATA_PATH” is an environment variable for the encrypted test set. After submission, you can access the test set for system scoring in the following manner, but the contestant cannot download it directly.-----#
if os.environ.get('DATA_PATH'):
DATA_PATH = os.environ.get("DATA_PATH") + "/"
else:
print("When baseline is running, this error message will appear because the test set cannot be read, which is a normal phenomenon.")
# When baseline is running, this error message will appear because the test set cannot be read, which is a normal phenomenon.
testA_path = DATA_PATH + "a_seq_testA.csv" # read testing setA
data_testA = pd.read_csv(testA_path)
testB_path = DATA_PATH + "a_seq_testB.csv" # read teseting setB
data_testB = pd.read_csv(testB_path)
#--------------Start Parameter Solving-------------#
p_train, q_train = pqregression(data_train)
p_testA, q_testA = pqregression(data_testA)
p_testB, q_testB = pqregression(data_testB)
p = [p_train,p_testA,p_testB]
q = [q_train,q_testA,q_testB]
#-----to save the parameters p and q to the submission.csv file-----#
df_params = pd.DataFrame({'p': p, 'q': q})
print(df_params)
csv_file_path = 'submission.csv'
df_params.to_csv(csv_file_path, index=False)
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