function [7:0]tv2cm; input [7:0]data_input; tv2cm = 8'h00; tv2cm = ~data_input[6:0] + 1; endfunction

module seg( input wire clk , //100MHz input wire rst_n , //low valid input wire [17:0] data_in , //待显示数据 output reg [6:0] hex1 , // -共阳极，低电平有效 output reg [6:0] hex2 , // - output reg [6:0] hex3 , // - output reg [6:0] hex4 //熄灭 ); //parameter define localparam NUM_0 = 8'b1100_0000, NUM_1 = 8'b1111_1001, NUM_2 = 8'b1010_0100, NUM_3 = 8'b1011_0000, NUM_4 = 8'b1001_1001, NUM_5 = 8'b1001_0010, NUM_6 = 8'b1000_0010, NUM_7 = 8'b1111_1000, NUM_8 = 8'b1000_0000, NUM_9 = 8'b1001_0000, NUM_A = 8'b1000_1000, NUM_B = 8'b1000_0011, NUM_C = 8'b1100_0110, NUM_D = 8'b1010_0001, NUM_E = 8'b1000_0110, NUM_F = 8'b1000_1110, ALL_LIGHT = 8'b0000_0000, LIT_OUT = 8'b1111_1111; //reg 、wire define reg [3:0] cm_hund ;//100cm reg [3:0] cm_ten ;//10cm reg [3:0] cm_unit ;//1cm always @(posedge clk or negedge rst_n)begin if(!rst_n)begin cm_hund <= 'd0; cm_ten <= 'd0; cm_unit <= 'd0; end else begin cm_hund <= data_in / 10 2; cm_ten <= data_in / 101 % 10; cm_unit <= data_in/10**0 % 10; end end always @(posedge clk or negedge rst_n)begin if(!rst_n)begin hex1 <= ALL_LIGHT; hex2 <= ALL_LIGHT; hex3 <= ALL_LIGHT; hex4 <= ALL_LIGHT; end else begin hex1 <= hex_data(cm_unit); hex2 <= hex_data(cm_ten); hex3 <= hex_data(cm_hund); hex4 <= LIT_OUT; end end //always end function [6:0] hex_data; //函数不含时序逻辑相关 input [03:00] data_i;//至少一个输入 begin case(data_i) 'd0:hex_data = NUM_0; 'd1:hex_data = NUM_1; 'd2:hex_data = NUM_2; 'd3:hex_data = NUM_3; 'd4:hex_data = NUM_4; 'd5:hex_data = NUM_5; 'd6:hex_data = NUM_6; 'd7:hex_data = NUM_7; 'd8:hex_data = NUM_8; 'd9:hex_data = NUM_9; default:hex_data = ALL_LIGHT; endcase end endfunction endmodule

其中，模块的输入包括时钟信号clk、复位信号rst_n和待显示的数据data_in，输出包括4个七段数码管的控制信号hex1、hex2、hex3和hex4。模块中使用了parameter定义常量和reg、wire定义寄存器和线网，同时使用了always块...

module seg( input wire clk , //100MHz input wire rst_n , //low valid input wire [9:0] data_in , //待显示数据 output reg [6:0] hex1 , // -共阳极，低电平有效 output reg [6:0] hex2 , // - output reg [6:0] hex3 // - ); //parameter define localparam NUM_0 = 8'b1100_0000, NUM_1 = 8'b1111_1001, NUM_2 = 8'b1010_0100, NUM_3 = 8'b1011_0000, NUM_4 = 8'b1001_1001, NUM_5 = 8'b1001_0010, NUM_6 = 8'b1000_0010, NUM_7 = 8'b1111_1000, NUM_8 = 8'b1000_0000, NUM_9 = 8'b1001_0000, NUM_A = 8'b1000_1000, NUM_B = 8'b1000_0011, NUM_C = 8'b1100_0110, NUM_D = 8'b1010_0001, NUM_E = 8'b1000_0110, NUM_F = 8'b1000_1110, ALL_LIGHT = 8'b0000_0000, LIT_OUT = 8'b1111_1111; //reg 、wire define reg [3:0] cm_hund ;//100cm reg [3:0] cm_ten ;//10cm reg [3:0] cm_unit ;//1cm always @(posedge clk or negedge rst_n)begin if(!rst_n)begin cm_hund <= 'd0; cm_ten <= 'd0; cm_unit <= 'd0; end else begin cm_hund <= data_in / 10 5; cm_ten <= data_in / 10 4 % 10; cm_unit <= data_in / 10 ** 3 % 10; end end always @(posedge clk or negedge rst_n)begin if(!rst_n)begin hex1 <= ALL_LIGHT; hex2 <= ALL_LIGHT; hex3 <= ALL_LIGHT; end else begin hex1 <= hex_data(cm_unit); hex2 <= hex_data(cm_ten); hex3 <= hex_data(cm_hund); end end //always end function [6:0] hex_data; //函数不含时序逻辑相关 input [03:00] data_i;//至少一个输入 begin case(data_i) 'd0:hex_data = NUM_0; 'd1:hex_data = NUM_1; 'd2:hex_data = NUM_2; 'd3:hex_data = NUM_3; 'd4:hex_data = NUM_4; 'd5:hex_data = NUM_5; 'd6:hex_data = NUM_6; 'd7:hex_data = NUM_7; 'd8:hex_data = NUM_8; 'd9:hex_data = NUM_9; default:hex_data = ALL_LIGHT; endcase end endfunction endmodule

这是一个Verilog代码模块，用于将输入的十进制数字转换为三个七段数码管的显示，其中包括一个时序逻辑相关的always块和一个不含时序逻辑相关的函数hex_data。在时序逻辑相关的always块中，使用一个三位数字将输入...

CREATE OR REPLACE FUNCTION Murmurhash3(input_data VARCHAR2, seed NUMBER) RETURN NUMBER IS h NUMBER := seed; c1 NUMBER := 3458204593; -- 0xcc9e2d51 c2 NUMBER := 1735298763; -- 0x1b873593 r1 NUMBER := 15; r2 NUMBER := 13; m NUMBER := 5; n NUMBER := 364292196; -- 0xe6546b64 blkNums NUMBER := LENGTH(input_data) / 4; remaining_bytes VARCHAR2(4); k NUMBER := 0; res NUMBER := 0; BEGIN FOR i IN 0..blkNums-1 LOOP res := 0; FOR j IN 0..3 LOOP res := res + ASCII(SUBSTR(input_data, i4+j+1, 1)) POWER(2, (3-j)8); END LOOP; res := res c1; res := MOD(res, POWER(2, 32)); res := BITAND(res, POWER(2, 32)-1); res := res || 0; res := BITAND(res, POWER(2, 32)-1); res := BITAND(res * POWER(2, r1), POWER(2, 32)-1); res := res || 0; res := BITAND(res, POWER(2, 32)-1); res := BITAND(res * c2, POWER(2, 32)-1); res := res || 0; res := BITAND(res, POWER(2, 32)-1); h := BITAND(hm + n, POWER(2, 32)-1); h := BITAND(h+res, POWER(2, 32)-1); END LOOP; remaining_bytes := SUBSTR(input_data, blkNums4+1); FOR i IN 1..LENGTH(remaining_bytes) LOOP k := k + ASCII(SUBSTR(remaining_bytes, i, 1)) * POWER(2, (4-i)8); END LOOP; k := k c1; k := MOD(k, POWER(2, 32)); k := BITAND(k, POWER(2, 32)-1); k := BITAND(k * POWER(2, r1), POWER(2, 32)-1); k := BITAND(k * c2, POWER(2, 32)-1); k := BITAND(k || 0, POWER(2, 32)-1); h := BITXOR(h, k); h := BITXOR(h, LENGTH(input_data)); h := BITAND(h, POWER(2, 32)-1); h := BITAND(h XOR (h SHR 16), POWER(2, 32)-1); h := BITAND(h * 2246822507, POWER(2, 32)-1); h := BITAND(h XOR (h SHR 13), POWER(2, 32)-1); h := BITAND(h * 3226489909, POWER(2, 32)-1); h := BITAND(h XOR (h SHR 16), POWER(2, 32)-1); RETURN h; END; /

- res := res + ASCII(SUBSTR(input_data, i*4+j+1, 1)) * POWER(2, (3-j)*8);：计算哈希值。 - END LOOP;：内层循环结束。 - res := res * c1;：乘以常量 c1。 - res := MOD(res, POWER(2, 32));：对 ...

ValueError: Input must be >= 2-d.

This error occurs when a function or method expects an input that is at least two-dimensional, but the input provided is either one-dimensional or is not numeric. For example, if you try to use a ...

function [f,p] = fft_plot(input_pre,Fs,SAM) L = FsSAM;%采样点数 input_p = input_pre(1:(floor(length(input_pre)/L)L)); P = zeros(L/2+1,floor(length(input_p)/L)); for i = 1:floor(length(input_p)/L) f = Fs(0:(L/2))/L; input = input_p((i-1)L+1:iL); primary_data = input-mean(input); primary_data = reshape(primary_data,[],1); primary_data = primary_data(1:L); %数据直接进行fft处理并plot pri_fft = fft(primary_data); P2 = abs(pri_fft/L); P1 = P2(1:L/2+1); P(2:end-1,i) = 2P1(2:end-1); end Pover = mean(P'); Pover = Pover'; plot(f,Pover); p = Pover; grid on hold on end

f = Fs*(0:(L/2))/L; % 将 f 的定义放在循环外面 for i = 1:floor(length(input_p)/L) input = input_p((i-1)*L+1:i*L); primary_data = input-mean(input); primary_data = reshape(primary_data,[],1); ...

ValueError: Must pass 2-d input. shape=(189, 3, 14)

This error message is indicating that the input data must have two dimensions, but it currently has three dimensions with shapes (189, 3, 14). To resolve this error, you can try reshaping the data ...

assign Sftm = Function_opcode[2:0]; // 实际有用的只有低三位(移位指令） assign Exe_code = (I_format==0) ? Function_opcode : {3'b000,Exe_opcode[2:0]}; assign Ainput = Read_data_1; assign Binput = (ALUSrc == 0) ? Read_data_2 : Sign_extend[31:0]; //R/LW,SW sft else的时候含LW和SW assign ALU_ctl[0] = (Exe_code[0] | Exe_code[3]) & ALUOp[1]; //24H AND assign ALU_ctl[1] = ((!Exe_code[2]) | (!ALUOp[1])); assign ALU_ctl[2] = (Exe_code[1] & ALUOp[1]) | ALUOp[0];

7. assign ALU_ctl[2] = (Exe_code[1] & ALUOp[1]) | ALUOp[0]; 将一个复合逻辑表达式的值分配给ALU_ctl的第2位。复合逻辑表达式是Exe_code的第1位与ALUOp的第1位的与运算，再与ALUOp的第0位进行或运算。

下面的这段python代码，哪里有错误，修改一下：import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from torch.autograd import Variable from sklearn.preprocessing import MinMaxScaler training_set = pd.read_csv('CX2-36_1971.csv') training_set = training_set.iloc[:, 1:2].values def sliding_windows(data, seq_length): x = [] y = [] for i in range(len(data) - seq_length): _x = data[i:(i + seq_length)] _y = data[i + seq_length] x.append(_x) y.append(_y) return np.array(x), np.array(y) sc = MinMaxScaler() training_data = sc.fit_transform(training_set) seq_length = 1 x, y = sliding_windows(training_data, seq_length) train_size = int(len(y) * 0.8) test_size = len(y) - train_size dataX = Variable(torch.Tensor(np.array(x))) dataY = Variable(torch.Tensor(np.array(y))) trainX = Variable(torch.Tensor(np.array(x[1:train_size]))) trainY = Variable(torch.Tensor(np.array(y[1:train_size]))) testX = Variable(torch.Tensor(np.array(x[train_size:len(x)]))) testY = Variable(torch.Tensor(np.array(y[train_size:len(y)]))) class LSTM(nn.Module): def init(self, num_classes, input_size, hidden_size, num_layers): super(LSTM, self).init() self.num_classes = num_classes self.num_layers = num_layers self.input_size = input_size self.hidden_size = hidden_size self.seq_length = seq_length self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, num_classes) def forward(self, x): h_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) # Propagate input through LSTM ula, (h_out, _) = self.lstm(x, (h_0, c_0)) h_out = h_out.view(-1, self.hidden_size) out = self.fc(h_out) return out num_epochs = 2000 learning_rate = 0.001 input_size = 1 hidden_size = 2 num_layers = 1 num_classes = 1 lstm = LSTM(num_classes, input_size, hidden_size, num_layers) criterion = torch.nn.MSELoss() # mean-squared error for regression optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate) # optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate) runn = 10 Y_predict = np.zeros((runn, len(dataY))) # Train the model for i in range(runn): print('Run: ' + str(i + 1)) for epoch in range(num_epochs): outputs = lstm(trainX) optimizer.zero_grad() # obtain the loss function loss = criterion(outputs, trainY) loss.backward() optimizer.step() if epoch % 100 == 0: print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) lstm.eval() train_predict = lstm(dataX) data_predict = train_predict.data.numpy() dataY_plot = dataY.data.numpy() data_predict = sc.inverse_transform(data_predict) dataY_plot = sc.inverse_transform(dataY_plot) Y_predict[i,:] = np.transpose(np.array(data_predict)) Y_Predict = np.mean(np.array(Y_predict)) Y_Predict_T = np.transpose(np.array(Y_Predict))

ula, (h_out, _) = self.lstm(x, (h_0, c_0)) h_out = h_out.view(-1, self.hidden_size) out = self.fc(h_out) return out # 定义训练参数 num_epochs = 2000 learning_rate = 0.001 input_size = 1 ...

function [f,p] = fft_plot(input_pre,Fs,SAM) L = FsSAM;%采样点数 input_p = input_pre(1:(floor(length(input_pre)/L)L)); P = zeros(L/2+1,floor(length(input_p)/L)); for i = 1:floor(length(input_p)/L) f = Fs(0:(L/2))/L; input = input_p((i-1)L+1:iL); primary_data = input-mean(input); primary_data = reshape(primary_data,[],1); primary_data = primary_data(1:L); %数据直接进行fft处理并plot pri_fft = fft(primary_data); P2 = abs(pri_fft/L); P1 = P2(1:L/2+1); P(2:end-1,i) = 2P1(2:end-1); end Pover = mean(P'); Pover = Pover'; plot(f,Pover); p = Pover; grid on hold on end 函数或变量 'f' 无法识别。出错 fft_plot (第 19 行) plot(f,Pover);

function [f,p] = fft_plot(input_pre,Fs,SAM) L = Fs*SAM;%采样点数 input_p = input_pre(1:(floor(length(input_pre)/L)*L)); P = zeros(L/2+1,floor(length(input_p)/L)); f = Fs*(0:(L/2))/L; % 将 f 的定义...

//XW_crc_p.v pragma protect begin module DW_crc_p( data_in, crc_in, crc_ok, crc_out ); parameter integer data_width = 16; parameter integer poly_size = 16; parameter integer crc_cfg = 7; parameter integer bit_order = 3; parameter integer poly_coef0 = 4129; parameter integer poly_coef1 = 0; parameter integer poly_coef2 = 0; parameter integer poly_coef3 = 0; input [data_width-1:0] data_in; input [poly_size-1:0] crc_in; output crc_ok; output [poly_size-1:0] crc_out; define DW_max_data_crc_1 (data_width>poly_size?data_width:poly_size) wire [poly_size-1:0] crc_in_inv; wire [poly_size-1:0] crc_reg; wire [poly_size-1:0] crc_out_inv; wire [poly_size-1:0] crc_chk_crc_in; reg [poly_size-1:0] crc_inv_alt; reg [poly_size-1:0] crc_polynomial; include "bit_order_crc_function.inc" include "bit_order_data_function.inc" include "calculate_crc_w_in_function.inc" include "calculate_crc_function.inc" include "calculate_crc_crc_function.inc" generate //begin genvar bit_idx; reg [63:0] crc_polynomial64; reg [15:0] coef0; reg [15:0] coef1; reg [15:0] coef2; reg [15:0] coef3; assign coef0= poly_coef0; assign coef0= poly_coef1; assign coef0= poly_coef2; assign coef0= poly_coef3; assign crc_polynomial64 = {coef3, coef2, coef1, coef0}; assign crc_pollynomial = crc_polynomial64[poly_size-1:0]; case(crc_cfg/2) 0: assign crc_inv_alt = {poly_size{1'b0}}; 1: for(bit_idx = 0; bit_idx<poly_sizel bit_idx=bit_idx+1) assign crc_inv_alt[bit_idx] = (bit_idx % 2)? 1'b0:1'b1; 2: for(bit_idx=0; bit_idx<poly_size; bit_idx=bit_idx+1) assign crc_inv_alt[bit_idx] = (bit_idx % 2)?1'b1:1'b0; 3: assign crc_inv_alt = { poly_size{1'b1}}; endcase endgenerate assign crc_in_inv = bit_order_crc(crc_in) ^ crc_inv_alt; assign crc_reg = calculate_crc(bit_order_data(data_in)); assign crc_out_inv = crc_reg; assign crc_out = bit_order_crc(crc_out_inv)^ crc_inv_alt; assign crc_chk_crc_in = calculate_crc_crc(crc_reg, crc_in_inv); assign crc_ok = (crc_chk_crc_in ==0); undef DW_max_data_crc_1 endmodule pragma protect end can you write a testbench for this piece of CRC verilog code so that this verilog file and the testbench can be used togerther by vcs to verify the correctness of this verilog file?

reg [15:0] data_in; reg [15:0] crc_in; // Outputs wire crc_ok; wire [15:0] crc_out; // Instantiate the DUT DW_crc_p dut ( .data_in(data_in), .crc_in(crc_in), .crc_ok(crc_ok), .crc_out(crc...

Input 0 of layer "conv1d" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (None, 5)

We reshape the input data using the reshape function to have a shape of (-1, 5, 1), where -1 signifies that the batch size can be variable. This reshaped data can then be passed to the Conv1D ...

Input In [9] def data= df.iloc[:, 1:-2].values ^ SyntaxError: invalid syntax

def my_function(my_parameter: str) -> int: # 在这里编写函数体 return 42 所以，对于你的代码，你需要将 = 换成 : ，就像这样： python def data(df: pd.DataFrame) -> np.ndarray: return df....

error: (-215:Assertion failed) (type == CV_32F || type == CV_8U) in function 'cv::batchDistance'

This error typically occurs when using the OpenCV function cv::batchDistance with an input array that has a data type other than CV_32F (32-bit floating point) or CV_8U (8-bit unsigned integer)....

class cpu_model; bit [31:0] r0, r1, r2, r3; bit [31:0] sram [256]; enum { ADD, LOAD, STORE } instruct_type; typedef struct packed { instruct_type type; bit [31:0] data; bit [7:0] addr; bit [1:0] reg1, reg2, dest_reg; } instruct; bit clk, reset; bit instr_valid, [31:0] instr_data; interface clk_rst_if; modport clk_rst ( input wire clk, reset, input wire instr_valid, input wire [31:0] instr_data ); endinterface interface bus_if; modport cpu_bus ( output reg [31:0] r0, r1, r2, r3, output reg [31:0] sram [256], input wire [31:0] instr_data, input wire reg1, reg2, dest_reg, input wire [7:0] addr, input wire instr_valid ); endinterface task drive_cpu (); input clk, reset, instr_valid; input [31:0] instr_data; clk_rst_if clk_rst(); bus_if cpu_bus(); begin r0 = r1 = r2 = r3 = 0; instr_valid = 0; instr_data = 0; forever begin @(posedge clk); if (reset) begin r0 = r1 = r2 = r3 = 0; end else if (instr_valid) begin instruct inst; inst = get_instr(instr_data); case (inst.type) ADD: r[inst.dest_reg] = r[inst.reg1] + r[inst.reg2]; LOAD: r[inst.dest_reg] = sram[inst.addr]; STORE: sram[inst.addr] = r[inst.reg1]; endcase end end end endtask task monitor (); input clk; clk_rst_if clk_rst(); bus_if cpu_bus(); begin @(posedge clk); $display("r0 = %d, r1 = %d, r2 = %d, r3 = %d", r0, r1, r2, r3); @(posedge clk); for (int i = 0; i < 256; i++) begin $display("sram[%0d] = %d", i, sram[i]); end end endtask function automatic instruct get_instr (input [31:0] instr_data); instruct inst; if (instr_data[31:30] == 2'b00) begin inst.type = ADD; inst.reg1 = instr_data[29:28]; inst.reg2 = instr_data[27:26]; inst.dest_reg = instr_data[25:24]; end else if (instr_data[31:30] == 2'b01) begin if (instr_data[26]) begin inst.type = LOAD; inst.data = instr_data[23:16]; inst.dest_reg = instr_data[11:8]; end else begin inst.type = LOAD; inst.addr = instr_data[7:0]; inst.dest_reg = instr_data[11:8]; end end else if (instr_data[31:30] == 2'b10) begin if (instr_data[26]) begin inst.type = STORE; inst.data = instr_data[23:16]; inst.reg1 = instr_data[11:8]; end else begin inst.type = STORE; inst.addr = instr_data[7:0]; inst.reg1 = instr_data[11:8]; end end return inst; endfunction endclass

这段代码是一个 CPU 模型，使用 SystemVerilog 语言编写。它定义了一个 cpu_model 类，包含寄存器、指令类型、指令结构体等成员，以及两个接口 clk_rst_if 和 bus_if。其中，drive_cpu() 和 monitor() 两个任务分别...

function [7:0]tv2cm; input [7:0]data_input; tv2cm = 8'h00; tv2cm = ~data_input[6:0] + 1; endfunction

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ValueError: Input must be >= 2-d.

ValueError: Must pass 2-d input. shape=(189, 3, 14)

Input 0 of layer "conv1d" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (None, 5)

Input In [9] def data= df.iloc[:, 1:-2].values ^ SyntaxError: invalid syntax

error: (-215:Assertion failed) (type == CV_32F || type == CV_8U) in function 'cv::batchDistance'

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