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1 | 1 | #include "data_provider.h" |
2 | 2 |
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3 | | - const float xmean = 192.0; //用于归一化的均值,在模型训练前算得 |
4 | | - const float xstd = 168.07; //用于归一化的标准差 |
5 | | - const float input_scale = 0.0; //模型量化参数 |
6 | | - const int input_zero_point = 0; //模型量化参数 |
7 | | - const int windows_len = 12; |
| 3 | +const float xmean = 192.0; //用于归一化的均值,在模型训练前算得 |
| 4 | +const float xstd = 168.07; //用于归一化的标准差 |
| 5 | +const float input_scale = 0.0; //模型量化参数 |
| 6 | +const int input_zero_point = 0; //模型量化参数 |
| 7 | +const int windows_len = 12; |
8 | 8 |
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9 | | -void get_data(ATT7053_DEF_PTR att7053 ,sample_data_node_ptr getdata){ |
| 9 | +void get_data(ATT7053_DEF_PTR att7053 ,sample_data_node_ptr getdata) |
| 10 | +{ |
10 | 11 | getdata->IRMS = Read_Reg(att7053,Current1_Rms_Register); |
11 | 12 | getdata->active_power = ((int32_t)Read_Reg(att7053,PowerP1_Register)<<16)>>16; |
12 | 13 | getdata->reactive_power = ((int32_t)Read_Reg(att7053,PowerQ1_Register)<<16)>>16; |
13 | 14 | getdata->apparent_power = Read_Reg(att7053,PowerS_Register); |
14 | 15 | } |
15 | 16 |
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16 | | -void data_container_init(sample_data_node data_cont[], uint32_t winlength){ |
| 17 | +void data_container_init(sample_data_node data_cont[], uint32_t winlength) |
| 18 | +{ |
17 | 19 | for(uint32_t i=0;i<(winlength-1);i++){ |
18 | 20 | data_cont[i].next_ptr = &data_cont[i+1]; |
19 | 21 | } |
20 | 22 | data_cont[winlength-1].next_ptr = data_cont; |
21 | 23 | } |
22 | 24 |
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23 | | -void data_provider(TfLiteTensor* input, sample_data_node_ptr data_buffer, int winlength){ |
| 25 | +void data_provider(TfLiteTensor* input, sample_data_node_ptr data_buffer, int winlength) |
| 26 | +{ |
24 | 27 | float *inputdata = input->data.f; |
25 | 28 | for(int i=0; i<winlength; i++){ |
26 | 29 | data_buffer = data_buffer->next_ptr; |
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