## Calculate the t-statistics t=(a.mean()-b.mean())/(s*np.sqrt(2/N))## Comparewiththe critical t-value #Degreesoffreedom df=2*N-2#p-value after comparisonwiththe t p=1-stats.t.cdf(t,df=df)print("t = "+str(t))print("p = "+str(2*p))#Note that we multiply the p v...
listbox.delete(deleteindex) def calculate_point(): if len(list_a)==0: messagebox.showerror("注意","请先输入数据") return sum1=0 sum2=0 for x in range(len(list_a)): if float(list_a[x])>=60.0: sum1=sum1+(float(list_a[x])-50)*float(list_b[x]) sum2=sum2+float(list_b...
print("Calculate the total score...") c.execute("SELECT StudentID, python_score, English_score from DK172") marks = c.fetchall() for mark in marks: total = mark[1] + mark[2] ID = mark[0] #print(mark[1], mark[2], total) c.execute("UPDATE DK172 SET Total = ? WHERE Studen...
rgb_to_hsv(r / 255.0, g / 255.0, b / 255.0)[1] y = min(abs(r * 2104 + g * 4130 + b * 802 + 4096 + 131072) >> 13, 235) y = (y - 16.0) / (235 - 16) # 忽略高亮色 if y > 0.9: continue # Calculate the score, preferring highly saturated colors. # Add 0.1 to...
score=float(match.group(1))# 假设学分可能是小数，所以转换为float类型returncredit_scoreelse:return...
_score_map, pk_grp):# 比大小for p in pk_grp: p["score"], p["type"] = calculate(_score_map, p["poker"]) print("开牌结果---")for p in pk_grp: print(p) print("赢家是---") best = max(pk_grp, key=lambda x: x["score"])["name"] print(best)return...
(*args)) return button_sprites_group '''计算'''def calculate(number1, number2, operator): operator_map = {'+': '+', '-': '-', '×': '*', '÷': '/'} try: result = str(eval(number1+operator_map[operator]+number2)) return result if '.' not in result else str(Fraction...
Calculate the BLEU Score Conclusion How to Calculate the BLEU Score Before proceeding to the BLEU score calculation, let's briefly look at n-grams. Let’s say you have a sentence: "I love to eat ice cream." An n-gram is just a fancy way of saying "a group of words." The n in ...
import pandas as pdimport datetime as dt# Convert to datetime and get today's dateusers['Birthday'] = pd.to_datetime(users['Birthday'])today = dt.date.today()# For each row in the Birthday column, calculate year diff...
train_size], X[train_size:]history = [x for x in train]# make predictionspredictions = list()for t in range(len(test)):model = ARIMA(history, order=arima_order)model_fit = model.fit(disp=0)yhat = model_fit.forecast()[0]predictions.append(yhat)history.append(test[t])# calculate ...