Published: April 18, 2022 Link：https://pubs.acs.org/doi/10.1021/acs.est.1c07638 摘要 环境DNA (eDNA)用于间接性物种检测分析的增加
":"d1","productname":"p1","unitcost":10.00,"status":"P","listprice":36.50,"attr1":"Large","itemid":"EST ","productname":"p2","unitcost":12.00,"status":"P","listprice":26.50,"attr1":"Rattleless","itemid":"EST ,"productname":"p2","unitcost":12.00,"status":"P","listprice":35.50,"attr1":"Green Adult","itemid":"EST -10"}, {"unitcost":10.00,"status":"P","listprice":38.50,"attr1":"Venomless","itemid":"EST -11"}, {"unitcost":12.00,"status":"P","listprice":26.50,"attr1":"Rattleless","itemid":"EST
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12.5] # fit a linear curve an estimate its y-values and their error. a, b = np.polyfit(x, y, deg=1) y_est (x - x.mean())**2 / np.sum((x - x.mean())**2)) fig, ax = plt.subplots() ax.plot(x, y_est , '-') ax.fill_between(x, y_est - y_err, y_est + y_err, alpha=0.2) ax.plot(x, y, 'o', color='tab:brown 12.5] # fit a linear curve an estimate its y-values and their error. a, b = np.polyfit(x, y, deg=1) y_est , '-') ax.fill_between(x, y_est - y_err, y_est + y_err, alpha=0.2) ax.plot(x, y, 'o', color='tab:brown
Dalmation","unitcost":12.00,"status":"P","listprice":18.50,"attr1":"Spotted Adult Female","itemid":"EST productname":"Iguana","unitcost":12.00,"status":"P","listprice":35.50,"attr1":"Green Adult","itemid":"EST ,"productname":"Manx","unitcost":12.00,"status":"P","listprice":158.50,"attr1":"Tailless","itemid":"EST ,"productname":"Manx","unitcost":12.00,"status":"P","listprice":83.50,"attr1":"With tail","itemid":"EST productname":"Persian","unitcost":12.00,"status":"P","listprice":89.50,"attr1":"Adult Male","itemid":"EST
dt$Treatment) dt$Placebo <- ifelse(is.na(dt$Placebo), "", dt$Placebo) dt$se <- (log(dt$hi) - log(dt$est 只需提供另一组est，lower和upper。如果提供的est、lower和upper的数目大于绘制CI的列号，则est、lower和upper将被重用。 如下例所示，est_gp1和est_gp2将画在第3列和第5列中。但是est_gp3和est_gp4还没有被使用，它们将再次被绘制到第3列和第5列。 因此，将est_gp1和est_gp2视为组1,est_gp3和est_gp4视为组2 # Add blank column for the second CI column dt$` ` <- paste = list(dt$est_gp1, dt$est_gp2, dt$est_gp3,
))+data_1(i)*b_est*cos(a_est*data_1(i)) -sin(b_est*data_1(i))*a_est*data_1(i)+sin(a_est*data_1(i)) ] ; % 雅可比矩阵由偏导组成 end % 根据当前参数，得到函数值 y_est = a_est*cos(b_est*data_1) + b_est*sin(a_est*data_1); % 计算误差 +dp(1); % 在初始值上加上所求步长，作为新的评估参数 b_lm=b_est+dp(2); % 计算新的可能估计值对应的y和计算残差e y_est_lm = a_lm*cos(b_lm*data =a_lm; b_est=b_lm; e=e_lm; disp(e); updateJ=1; end else updateJ=0; lamda=lamda*5; end end %显示优化的结果 a_est b_est plot(data_1,obs_1,'r') hold on plot(data_1,a_est*cos(b_est*data_1) + b_est*sin(a_est*data_1),'
Perferendis temporibus alias eligendi quas ullam atque numquam repudiandae est minima doloribus!" Perferendis temporibus alias eligendi quas ullam atque numquam repudiandae est minima doloribus!" Perferendis temporibus alias eligendi quas ullam atque numquam repudiandae est minima doloribus!" Perferendis temporibus alias eligendi quas ullam atque numquam repudiandae est minima doloribus!" Perferendis temporibus alias eligendi quas ullam atque numquam repudiandae est minima doloribus!"
16:41:21 SQL> select INST_ID, OPERATION, STATE, POWER, SOFAR, EST_WORK, EST_RATE, EST_MINUTES from GV 的值变为了24分钟： 16:50:25 SQL> / INST_ID OPERA STAT POWER SOFAR EST_WORK EST_RATE EST_MINUTES 的值变为了0. 17:16:54 SQL> / INST_ID OPERA STAT POWER SOFAR EST_WORK EST_RATE EST_MINUTES EST_WORK EST_RATE EST_MINUTES ---------- ----- ---- ---------- ---------- ---------- ---------- 的值变为0. 17:39:05 SQL> / INST_ID OPERA STAT POWER SOFAR EST_WORK EST_RATE EST_MINUTES
(X_train, y_train) print(est_gp. 最后把符号回归和决策树、随机森林训练的结果做一个对比 # 决策树、随机森林 est_tree = DecisionTreeRegressor() est_tree.fit(X_train, y_train ) est_rf = RandomForestRegressor(n_estimators=10) est_rf.fit(X_train, y_train) y_gp = est_gp.predict (np.c_[x0.ravel(), x1.ravel()]).reshape(x0.shape) score_gp = est_gp.score(X_test, y_test) y_tree = est_tree.predict est_rf.predict(np.c_[x0.ravel(), x1.ravel()]).reshape(x0.shape) score_rf = est_rf.score(X_test, y_test
ORA$AT_OS_OPT_SY_3926 SUCCEEDED 22-NOV-17 10.00.02.384206 PM EST5EDT ORA$AT_OS_OPT_SY_3946 SUCCEEDED 23-NOV-17 10.00.02.078143 PM EST5EDT ORA$AT_OS_OPT_SY_3966 SUCCEEDED 24-NOV-17 10.00.02.684644 PM EST5EDT ORA$AT_OS_OPT_SY_3986 SUCCEEDED 25-NOV-17 06.00.02.592675 AM EST5EDT ORA$AT_OS_OPT_SY_4006 SUCCEEDED 25-NOV-17 10.02.37.976591 AM EST5EDT
i=1:length(data_1) J(i,:)=[cos(b_est*data_1(i))+data_1(i)*b_est*cos(a_est*data_1(i)) -sin(b_est*data _1(i))*a_est*data_1(i)+sin(a_est*data_1(i)) ]; end % 根据当前参数，得到函数值 y_est = a_est*cos(b_est*data_1) + b_est *sin(a_est*data_1); % 计算误差 d=obs_1-y_est; % 计算(拟)海塞矩阵 H=J’*J; % 若是第一次迭代，计算误差 if it==1 e=dot(d,d); end +dp(1); b_lm=b_est+dp(2); % 计算新的可能估计值对应的y和计算残差e y_est_lm = a_lm*cos(b_lm*data_1) + b_lm*sin(a_lm*data %显示优化的结果 a_est b_est plot(data_1,obs_1,’r’) hold on plot(data_1,a_est*cos(b_est*data_1) + b_est*sin(
i d es t </w>': 3} 出现最频繁的字节对是es和t，共出现了6+3=9次，因此将它们合并 {'l o w </w>': 5, 'l o w e r </w>': 2, 'n e w est </w>': 6, 'w i d est </w>': 3} 出现最频繁的字节对是est和</w>，共出现了6+3=9次，因此将它们合并 {'l o w </w>': 5, 'l o w e r </ w>': 2, 'n e w est</w>': 6, 'w i d est</w>': 3} 出现最频繁的字节对是l和o，共出现了5+2=7次，因此将它们合并 {'lo w </w>': 5, 'lo w e r </w>': 2, 'n e w est</w>': 6, 'w i d est</w>': 3} 出现最频繁的字节对是lo和w，共出现了5+2=7次，因此将它们合并 {'low </w> ': 5, 'low e r </w>': 2, 'n e w est</w>': 6, 'w i d est</w>': 3} ......继续迭代直到达到预设的subwords词表大小或下一个最高频的字节对出现频率为
Voluptates, deserunt facilis et iste corrupti omnis tenetur est. Voluptates, deserunt facilis et iste corrupti omnis tenetur est. Voluptates, deserunt facilis et iste corrupti omnis tenetur est. Voluptates, deserunt facilis et iste corrupti omnis tenetur est. Voluptates, deserunt facilis et iste corrupti omnis tenetur est.
关于实例恢复统计，这块没什么可讲的 关于缓冲池建议 Size for Est(M) Oracle估算Buffer pool的大小 Size Factor 估算值和实际值的一个比例，比如0.9就是估算值是实际大小的 ，1.0表示实际的物理读 Estimated Physical Reads 估算的物理读次数 这部分，主要从Size Factor、Est Phys Read Factor 都等于1.00的行开始， 然后往上看，观察当Size Factor减小时，Est Phys Read Factor是不是明显变化，如果变化不明显，说明可以减小当前的buffer pool设置，相反则表示不能减小；然后往下看，观察当 Size Factor增大时，Est Phys Read Factor是不是明显变化，如果变化不明显，说明没必要增大buffer pool设置，相反，则表示增大buffer pool可以提高系统性能。 从图中可以看出，Buffer Pool设置为4G左右，Est Phys Read Factor已明显降低，设置为8G左右，边际效益已经很低了。
, "unitcost": 12.00, "status": "P", "listprice": 18.50, "attr1": "Spotted Adult Female", "itemid": "EST Rattlesnake", "unitcost": 12.00, "status": "P", "listprice": 38.50, "attr1": "Venomless", "itemid": "EST "Iguana", "unitcost": 12.00, "status": "P", "listprice": 35.50, "attr1": "Green Adult", "itemid": "EST Persian", "unitcost": 12.00, "status": "P", "listprice": 23.50, "attr1": "Adult Female", "itemid": "EST "Persian", "unitcost": 12.00, "status": "P", "listprice": 89.50, "attr1": "Adult Male", "itemid": "EST
[root@localhost ~]# date ; sleep 30 ; date Thu Nov 11 05:06:44 EST 2021 Thu Nov 11 05:07:14 EST 2021 = 1 minute 2h = 2 hours 3d = 3 days [root@localhost ~]# date ; sleep 10s ; date Thu Nov 11 05:11:09 EST 2021 Thu Nov 11 05:11:19 EST 2021 睡眠时间也可以小于1秒钟，就是在数字前面加一个.. .1 = 十分之一秒 .01 = 百分之一秒 .001 = 千分之一秒 [root @localhost ~]# date ; sleep .1 ; date Thu Nov 11 05:14:03 EST 2021 Thu Nov 11 05:14:03 EST 2021 sleep
(Y_train, T_train, X=X_train, W=W_train) te_pred = est.effect(X_test) # 模型 est1 = SparseLinearDML(model_y .fit(Y_train, T_train, X=X_train, W=W_train) te_pred1 = est1.effect(X_test) # 模型 est2 = DML(model_y= .fit(Y_train, T_train, X=X_train, W=W_train) te_pred2 = est2.effect(X_test) # 模型 est3 = CausalForestDML = est3.effect(X_test) # 画图 plt.figure(figsize=(10,6)) plt.plot(X_test, te_pred, label='DML default' =(conf_ints[1] - est.intercept_, est.intercept_ -
w e r </w>': 2, 'n e w es t </w>': 6, 'w i d es t </w>': 3} Iter 2, 最高频连续字节对"es"和"t"出现了6+3=9次, 合并成"est 输出： {'l o w </w>': 5, 'l o w e r </w>': 2, 'n e w est </w>': 6, 'w i d est </w>': 3} Iter 3, 以此类推，最高频连续字节对为 "est"和"</w>" 输出： {'l o w </w>': 5, 'l o w e r </w>': 2, 'n e w est</w>': 6, 'w i d est</w>': 3} Iter ', '</w>') # ('l', 'o') # ('lo', 'w') # ('n', 'e') # ('ne', 'w') # ('new', 'est</w>') # ('low', '</w> ') # ('w', 'i') # ('wi', 'd') # ('wid', 'est</w>') # ('low', 'e') # ('lowe', 'r') # ('lower', '</w>')
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