def write(self, vals): audit = self.env['approval.flow'].search([('approval_cannot_run_id', '=', '修改')]) value_list = list(vals.keys()) receive_list = [] value_list_not_in_receive_list = [i for i in value_list if i not in receive_list] value_list = value_list_not_in_receive_list # 应付 dispatch_number = 0 dispatch_id = self.env['roke.dispatch.order.management.price.approve'].search([('order_id.id', '=', self.id)]) dispatch_list = ['value03', 'value04', 'value05', 'value06', 'value07', 'value08'] if dispatch_id: for rec in value_list: for dispatch_list_id in dispatch_list: if rec == dispatch_list_id: if dispatch_id.approval_status != '未提' and dispatch_id.approval_status != '驳回': raise UserError("应付费用已开始审核不允许修改") value_list_not_in_dispatch_list = [i for i in value_list if i not in dispatch_list] value_list = value_list_not_in_dispatch_list dispatch_number = 1 break if dispatch_number: break # 基础 base_number = 0 base_list = ['client', 'department_manager', 'box_number', 'shipping_companies', 'field_station', 'ship_name_voyage', 'port_of_discharge', 'box_type', 'code', 'bill__of_lading_number', 'departure_date', 'lead_sealing', 'booking_time', 'date_of_pick_up', 'encore_date', 'set_port_date', 'estimated_range', 'destination_port_arrival_time'] if value_list: for rec in value_list: for base_list_id in base_list: if rec == base_list_id: if dispatch_id: if dispatch_id.approval_status != '未提' and dispatch_id.approval_status != '驳回': raise UserError("应付费用已开始审核不允许修改") base_number = 1 break if base_number: break return super(Rokedispatchordermanagement, self).write(vals) audit审核完成如果是修改不能修改如果不是可以修改怎么写可以实现

class PPOMemory: def init(self, batch_size): self.states = [] self.probs = [] self.vals = [] self.actions = [] self.rewards = [] self.dones = [] self.batch_size = batch_size def sample(self): batch_step = np.arange(0, len(self.states), self.batch_size) indices = np.arange(len(self.states), dtype=np.int64) np.random.shuffle(indices) batches = [indices[i:i+self.batch_size] for i in batch_step] return np.array(self.states),np.array(self.actions),np.array(self.probs),\ np.array(self.vals),np.array(self.rewards),np.array(self.dones),batches def push(self, state, action, probs, vals, reward, done): self.states.append(state) self.actions.append(action) self.probs.append(probs) self.vals.append(vals) self.rewards.append(reward) self.dones.append(done) def clear(self): self.states = [] self.probs = [] self.actions = [] self.rewards = [] self.dones = [] self.vals = []

3. 存储函数 push：将交互数据（即一个状态 state、一个动作 action、一个概率 probs、一个价值 vals、一个奖励 reward 和一个完成标志 done）存储到经验回放缓存中。 4. 清空函数 clear：清空经验回放缓存，以便下...

def draw_stats(self, vals, vals1, vals2, vals3, vals4, vals5, vals6): self.ax1 = plt.subplot(self.gs[0, 0]) self.ax1.plot(vals) self.ax1.set_xlim(self.xlim) locs = self.ax1.get_xticks() locs[0] = self.xlim[0] locs[-1] = self.xlim[1] self.ax1.set_xticks(locs) self.ax1.use_sticky_edges = False self.ax1.set_title(f'Connected Clients Ratio') self.ax2 = plt.subplot(self.gs[1, 0]) self.ax2.plot(vals1) self.ax2.set_xlim(self.xlim) self.ax2.set_xticks(locs) self.ax2.yaxis.set_major_formatter(FuncFormatter(format_bps)) self.ax2.use_sticky_edges = False self.ax2.set_title('Total Bandwidth Usage') self.ax3 = plt.subplot(self.gs[2, 0]) self.ax3.plot(vals2) self.ax3.set_xlim(self.xlim) self.ax3.set_xticks(locs) self.ax3.use_sticky_edges = False self.ax3.set_title('Bandwidth Usage Ratio in Slices (Averaged)') self.ax4 = plt.subplot(self.gs[3, 0]) self.ax4.plot(vals3) self.ax4.set_xlim(self.xlim) self.ax4.set_xticks(locs) self.ax4.use_sticky_edges = False self.ax4.set_title('Client Count Ratio per Slice') self.ax5 = plt.subplot(self.gs[0, 1]) self.ax5.plot(vals4) self.ax5.set_xlim(self.xlim) self.ax5.set_xticks(locs) self.ax5.use_sticky_edges = False self.ax5.set_title('Coverage Ratio') self.ax6 = plt.subplot(self.gs[1, 1]) self.ax6.plot(vals5) self.ax6.set_xlim(self.xlim) self.ax6.set_xticks(locs) self.ax6.yaxis.set_major_formatter(FormatStrFormatter('%.3f')) self.ax6.use_sticky_edges = False self.ax6.set_title('Block ratio') self.ax7 = plt.subplot(self.gs[2, 1]) self.ax7.plot(vals6) self.ax7.set_xlim(self.xlim) self.ax7.set_xticks(locs) self.ax7.yaxis.set_major_formatter(FormatStrFormatter('%.3f')) self.ax7.use_sticky_edges = False self.ax7.set_title('Handover ratio')修改为一张张输出图片

def draw_stats(self, vals, vals1, vals2, vals3, vals4, vals5, vals6, save_path): fig, axs = plt.subplots(4, 2, figsize=(16, 16)) axs[0, 0].plot(vals) axs[0, 0].set_xlim(self.xlim) locs = axs[0, 0]...

@api.model def create(self, vals): if vals.get("code") in ('/', None, False): vals["code"] = self.env['ir.sequence'].next_by_code('roke.production.order.code') # app创建处理默认为客户（防止用户创建客户不勾选是否客户） if vals.contains("customer_id"): partner_id = self.env['roke.partner'].search([('id', '=', int(vals.get('customer_id')))], limit=1) if partner_id: partner_id.write({'customer': True}) return super(RokeProductionOrder, self).create(vals)

接着判断传入的参数中是否包含 customer_id 字段，如果有，则将对应的 partner 记录标记为客户。最后调用 super() 方法创建 RokeProductionOrder 记录并返回。这个方法的作用是自动生成 code 值并将对应的 partner ...

def create_patrol_plan(cls, vals) -> dict: model_env = request.env[cls.models].sudo() data = request.jsonrequest or {} # 校验 for i in data: patrol_date = i.get('patrol_date') obj = model_env.search([('patrol_date', '=', patrol_date)]) if obj: # 已存在则修改 vals.update({'id': obj.id}) cls.home_public_write_dict(cls.models, vals) continue # 新增 create_data = cls.home_public_create_dict(model=cls.models, data=i) model_env.create(create_data) return 1该功能有什么缺陷

4. 函数中的cls.home_public_write_dict和cls.home_public_create_dict方法未给出其实现细节和输入输出，难以判断其是否符合要求和最佳实践。为了提高代码的可维护性、可扩展性和安全性，建议对函数进行重构和...

def draw_stats(self, vals): self.ax1 = plt.subplot(1, 1, 1) self.ax1.plot(vals) self.ax1.set_xlim(self.xlim) locs = self.ax1.get_xticks() locs[0] = self.xlim[0] locs[-1] = self.xlim[1] self.ax1.set_xticks(locs) self.ax1.use_sticky_edges = False self.ax1.set_title(f'Connected Clients Ratio') plt.savefig('output.png', dpi=300) 中添加x轴y轴标签

def draw_stats(self, vals): self.ax1 = plt.subplot(1, 1, 1) self.ax1.plot(vals) self.ax1.set_xlim(self.xlim) locs = self.ax1.get_xticks() locs[0] = self.xlim[0] locs[-1] = self.xlim[1] self.ax1...

import numpy as np def pca(X, threshold): # 去均值 X_mean = np.mean(X, axis=0) X = X - X_mean # 计算协方差矩阵 cov = np.dot(X.T, X) / (X.shape[0] - 1) # 计算特征值和特征向量 eig_vals, eig_vecs = np.linalg.eig(cov) # 对特征值进行排序 eig_vals_sort = np.argsort(eig_vals)[::-1] # 计算累计贡献率 eig_vals_sum = np.sum(eig_vals) cumsum = np.cumsum(eig_vals[eig_vals_sort]) / eig_vals_sum # 寻找最佳的n_components best_n_components = np.argmax(cumsum >= threshold) + 1 # 提取前best_n_components个特征向量 eig_vecs_sort = eig_vecs[:, eig_vals_sort[:best_n_components]] # 将数据投影到新的特征空间上 X_pca = np.dot(X, eig_vecs_sort) return X_pca # 生成数据集 data = np.random.rand(643, 1024) # 进行PCA降维 X_pca = pca(data, threshold=0.9) # 输出结果print("最佳的n_components为：", X_pca.shape[1])中threshold=0.9是怎么算出来的

在这段代码中，threshold=0.9 是作为一个参数传入函数pca()中的，它代表着累计贡献率的阈值，用于确定保留多少个主成分。在该函数中，累计贡献率是通过计算特征值的和来计算的，然后通过计算每个特征值在特征值总和...

def draw_stats(self, vals, vals1, vals2, vals3, vals4, vals5, vals6): self.ax1 = plt.subplot(1, 1, 1) # self.ax1 = plt.subplot(self.gs[0, 0]) self.ax1.plot(vals) self.ax1.set_xlim(self.xlim) locs = self.ax1.get_xticks() locs[0] = self.xlim[0] locs[-1] = self.xlim[1] self.ax1.set_xticks(locs) self.ax1.use_sticky_edges = False self.ax1.set_title(f'Connected Clients Ratio') plt.xlabel('Time (s)') plt.ylabel('Temperature (°C)') 正确吗

def draw_stats(self, time_vals, temp_vals): fig, ax = plt.subplots() ax.plot(time_vals, temp_vals) ax.set_title('Temperature vs. Time') ax.set_xlabel('Time (s)') ax.set_ylabel('Temperature (°C)'...

y_pred = np.zeros(y_train.shape) for tree in forest: a = [] for j in range(X_train.shape[1]): if np.median(X_train[:, j]) > np.mean(X_train[:, j]): fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])]) else: fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.median(X_train[:, j]), np.mean(X_train[:, j]), np.max(X_train[:, j])]) a.append(fuzzy_vals) fuzzy_vals = np.array(a).T y_pred += tree.predict_proba(fuzzy_vals)[:, 1] y_pred /= n_trees怎么改成分类数据的输出

fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])]) else: fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_...

y_pred = np.zeros((y_train.shape[0], 3)) for tree in forest: a = [] for j in range(X_train.shape[1]): if np.median(X_train[:, j]) > np.mean(X_train[:, j]): fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])]) else: fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.median(X_train[:, j]), np.mean(X_train[:, j]), np.max(X_train[:, j])]) a.append(fuzzy_vals) fuzzy_vals = np.array(a).T y_pred += tree.predict_proba(fuzzy_vals) y_pred /= n_trees改成三分类预测

然后，在第9行的 y_pred /= n_trees 之前，你需要将 tree.predict_proba(fuzzy_vals) 改成 tree.predict(fuzzy_vals)。这是因为 predict_proba 函数是用于返回每个类别的概率，而 predict 函数则返回每个...

改成三分类代码n_trees = 100 max_depth = 10 forest = [] for i in range(n_trees): idx = np.random.choice(X_train.shape[0], size=X_train.shape[0], replace=True) X_sampled = X_train[idx, :] y_sampled = y_train[idx] X_fuzzy = [] for j in range(X_sampled.shape[1]): if np.median(X_sampled[:, j])> np.mean(X_sampled[:, j]): fuzzy_vals = fuzz.trapmf(X_sampled[:, j], [np.min(X_sampled[:, j]), np.mean(X_sampled[:, j]), np.median(X_sampled[:, j]), np.max(X_sampled[:, j])]) else: fuzzy_vals = fuzz.trapmf(X_sampled[:, j], [np.min(X_sampled[:, j]), np.median(X_sampled[:, j]), np.mean(X_sampled[:, j]), np.max(X_sampled[:, j])]) X_fuzzy.append(fuzzy_vals) X_fuzzy = np.array(X_fuzzy).T tree = RandomForestClassifier(n_estimators=1, max_depth=max_depth) tree.fit(X_fuzzy, y_sampled) forest.append(tree) inputs = keras.Input(shape=(X_train.shape[1],)) x = keras.layers.Dense(64, activation="relu")(inputs) x = keras.layers.Dense(32, activation="relu")(x) outputs = keras.layers.Dense(1, activation="sigmoid")(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"]) y_pred = np.zeros(y_train.shape) for tree in forest: a = [] for j in range(X_train.shape[1]): if np.median(X_train[:, j]) > np.mean(X_train[:, j]): fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])]) else: fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.median(X_train[:, j]), np.mean(X_train[:, j]), np.max(X_train[:, j])]) a.append(fuzzy_vals) fuzzy_vals = np.array(a).T y_pred += tree.predict_proba(fuzzy_vals)[:, 1] y_pred /= n_trees model.fit(X_train, y_pred, epochs=10, batch_size=32) y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) rmse = math.sqrt(mse) print('RMSE:', rmse) print('Accuracy:', accuracy_score(y_test, y_pred))

这段代码实现了一个使用模糊隶属度将原始特征转换为模糊特征的随机森林分类器，并将其与一个神经网络模型进行集成。与之前不同的是，这里使用的是三分类问题，即目标变量有三个可能的取值。具体来说，代码首先定义...

帮我改进一这段代码import machine import time from machine import I2C from machine import Pin from machine import sleep class accel(): def init(self, i2c, addr=0x68): self.iic = i2c self.addr = addr self.iic.start() self.iic.writeto(self.addr, bytearray([107, 0])) self.iic.stop() def get_raw_values(self): self.iic.start() a = self.iic.readfrom_mem(self.addr, 0x3B, 14) self.iic.stop() return a def get_ints(self): b = self.get_raw_values() c = [] for i in b: c.append(i) return c def bytes_toint(self, firstbyte, secondbyte): if not firstbyte & 0x80: return firstbyte << 8 | secondbyte return - (((firstbyte ^ 255) << 8) | (secondbyte ^ 255) + 1) def get_values(self): raw_ints = self.get_raw_values() vals = {} vals["AcX"] = self.bytes_toint(raw_ints[0], raw_ints[1]) vals["AcY"] = self.bytes_toint(raw_ints[2], raw_ints[3]) vals["AcZ"] = self.bytes_toint(raw_ints[4], raw_ints[5]) vals["Tmp"] = self.bytes_toint(raw_ints[6], raw_ints[7]) / 340.00 + 36.53 vals["GyX"] = self.bytes_toint(raw_ints[8], raw_ints[9]) vals["GyY"] = self.bytes_toint(raw_ints[10], raw_ints[11]) vals["GyZ"] = self.bytes_toint(raw_ints[12], raw_ints[13]) return vals # returned in range of Int16 # -32768 to 32767 def val_test(self): # ONLY FOR TESTING! Also, fast reading sometimes crashes IIC from time import sleep while 1: print(self.get_values()) sleep(0.05) clk = Pin(("clk", 36), Pin.OUT_OD) sda = Pin(("sda", 37), Pin.OUT_OD) i2c = I2C(-1, clk, sda, freq=100000) #initializing the I2C method for ESP32 #i2c = I2C(scl=Pin(5), sda=Pin(4)) #initializing the I2C method for ESP8266 mpu= accel(i2c) while True: mpu.get_values() print(mpu.get_values()) time.sleep(2)

self.iic.writeto(self.addr, bytearray([107, 0])) self.iic.stop() def get_raw_values(self): """ Reads the raw values from the accelerometer and returns them as bytes. """ self.iic.start() ...

self.ax1 = plt.subplot(self.gs[0, 0]) self.ax1.plot(vals) self.ax1.set_xlim(self.xlim) locs = self.ax1.get_xticks() locs[0] = self.xlim[0] locs[-1] = self.xlim[1] self.ax1.set_xticks(locs) self.ax1.use_sticky_edges = False self.ax1.set_title(f'Connected Clients Ratio') 单独输出

接下来，将 vals 序列传递给 plot() 方法来绘制线性图，并使用 set_xlim() 方法和 get_xticks() 方法来设置 x 轴的范围和刻度位置。然后，将 use_sticky_edges 属性设置为 False，这样就可以更好地控制图形的布局。...

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