现在我们可以计算出Baseline的性能: # Function to calculate mean absolute error def mae(y_true, y_pred): baseline_guess = np.median(y) print('The baseline guess is a score of %0.2f' % baseline_guess) print("Baseline Performance on the test set: MAE = %0.4f" % mae(y_test, baseline_...
复制 #use numpy mean function to calculate the mean of the resultprint(round(np.mean(result),2)) 1. 2. 输出188.41。当然,您会得到略有不同的结果,因为这些是随机每日回报抽取的模拟。您在每次模拟中包含的路径或运行次数越多,平均值就越倾向于我们用作“mu”输入的平均回报。这是大数定律的结果。 我...
calculate_average(["x", None, ]) def test_empty_input(): with pytest.raises(ValueError): calculate_average([]) 扩展应用时可集成类型提示提升代码可读性。添加文档字符串说明参数要求与返回值类型,便于团队协作开发。对于需要高精度计算的场景,可引入decimal模块处理浮点精度问题。当涉及海量数据时,结合多线程...
self.y = ydefreset(self):"Reset the point back to the geometric origin: 0, 0"self.move(0,0)defcalculate_distance(self, other_point):"""Calculate the distance from this point to a second point passed as a parameter. This function uses the Pythagorean Theorem to calculate the distance be...
5. Handle function is not implemented for this dtype: [how->mean,dtype->object] using a custom function For columns with mixed data types where we still need to calculate mean or other statistical functions, consider writing custom functions. ...
X_mean=X.mean(axis=0)# calculate variance X_std=X.std(axis=0)# standardizeXX1=(X-X_mean)/X_std # 自己计算 # usefunctionpreprocessing.scale to standardizeXX_scale=preprocessing.scale(X)# 调用sklearn包的方法 # 最终X1与X_scale等价 ...
from scipy import stats alpha = 0.05 df = 5 t_stat = 1.96 # calculate the one tail critical value, equivalent to Excel TINV(alpha,df) cv = stats.t.ppf(1 - alpha, df) # ppf means percent point function (inverse of cdf — percentiles). # calculate the two tail critical value cv ...
代码:defcalculate_ichimoku_cloud(df):# Tenkan-sen (Conversion Line)nine_period_high = df['high'].rolling(window=9).max() nine_period_low = df['low'].rolling(window=9).min() df['tenkan_sen'] = (nine_period_high + nine_period_low) /2# Kijun-sen (Base Line)twenty_six_period_hi...
mean = sum(data) / n. squared_diff_sum = sum((x mean) 2 for x in data). if is_sample: return math.sqrt(squared_diff_sum / (n 1)). else: return math.sqrt(squared_diff_sum / n). data = [1, 2, 3, 4, 5]. 计算样本标准偏差。 sample_std_dev = calculate_std_dev(data,...
Nice case, according to the OpenCV documentation once you have the contours you should be able to calculate what you want using cv.arcLength() method. It is also called arc length. It can be found out using cv.arcLength() function. Second argument specify whether shape is a closed contour...