通过灰度值求苹果的甜度值 公式如下 x为灰度值 ,甜度值=13509-419x+4.88x^2-0.02524x^3+4.89x^4 帮我写一个kotlin 代码 传入灰度值返回甜度值
时间: 2023-10-15 22:02:08 浏览: 34
当然可以,以下是 Kotlin 代码实现:
```kotlin
fun calculateSweetness(grayScale: Double): Double {
val a = 13509.0
val b = 419.0
val c = 4.88
val d = 0.02524
val e = 4.89
val x = grayScale
val sweetness = a - b*x + c*x*x - d*x*x*x + e*x*x*x*x
return sweetness
}
```
你可以传入一个 Double 类型的灰度值,函数会返回一个 Double 类型的甜度值。
相关问题
计算这些预测值与目标值1531的误差(百分比):1713.8 1756.07 1586.33 1705.12 1559.34 1724.78 1471.79 1605.83 1713.30 1743.32
### 回答1:
首先,我们需要计算这些预测值与目标值的差值:
1531 - 1713.8 = -182.8
1531 - 1756.07 = -225.07
1531 - 1586.33 = -55.33
1531 - 1705.12 = -174.12
1531 - 1559.34 = 28.66
1531 - 1724.78 = -193.78
1531 - 1471.79 = 59.21
1531 - 1605.83 = -74.83
1531 - 1713.30 = -182.3
1531 - 1743.32 = -212.32
然后,我们可以计算这些差值除以目标值1531的百分比:
-182.8 / 1531 = -0.1194 = -11.94%
-225.07 / 1531 = -0.1469 = -14.69%
-55.33 / 1531 = -0.0361 = -3.61%
-174.12 / 1531 = -0.1137 = -11.37%
28.66 / 1531 = 0.0187 = 1.87%
-193.78 / 1531 = -0.1265 = -12.65%
59.21 / 1531 = 0.0387 = 3.87%
-74.83 / 1531 = -0.0489 = -4.89%
-182.3 / 1531 = -0.1191 = -11.91%
-212.32 / 1531 = -0.1388 = -13.88%
因此,这些预测值与目标值的误差分别为: -11.94%、-14.69%、-3.61%、-11.37%、1.87%、-12.65%、3.87%、-4.89%、-11.91%、-13.88%。
### 回答2:
要计算这些预测值与目标值1531的误差百分比,首先需要求每个预测值与目标值之间的差异。然后将差异值除以目标值,再乘以100,即可得到误差百分比。
首先计算每个预测值与目标值的差异:
1713.8 - 1531 = 182.8
1756.07 - 1531 = 225.07
1586.33 - 1531 = 55.33
1705.12 - 1531 = 174.12
1559.34 - 1531 = 28.34
1724.78 - 1531 = 193.78
1471.79 - 1531 = -59.21
1605.83 - 1531 = 74.83
1713.30 - 1531 = 182.30
1743.32 - 1531 = 212.32
接下来计算每个差异值除以目标值,并乘以100,即可得到误差百分比:
182.8 / 1531 * 100 = 11.95%
225.07 / 1531 * 100 = 14.69%
55.33 / 1531 * 100 = 3.61%
174.12 / 1531 * 100 = 11.37%
28.34 / 1531 * 100 = 1.85%
193.78 / 1531 * 100 = 12.66%
-59.21 / 1531 * 100 = -3.87%
74.83 / 1531 * 100 = 4.88%
182.30 / 1531 * 100 = 11.91%
212.32 / 1531 * 100 = 13.86%
因此,这些预测值与目标值1531的误差百分比分别为:11.95%, 14.69%, 3.61%, 11.37%, 1.85%, 12.66%, -3.87%, 4.88%, 11.91%, 13.86%。
### 回答3:
预测值与目标值1531之间的误差百分比可以通过以下步骤计算:
首先,我们需要计算每个预测值与目标值的差异(预测值-目标值),然后将这个差异值除以目标值1531,最后将结果乘以100,即可得到每个预测值与目标值之间的误差百分比。
具体计算如下:
预测值1713.8与目标值1531的误差百分比为:((1713.8-1531)/1531) * 100 = 12%
预测值1756.07与目标值1531的误差百分比为:((1756.07-1531)/1531) * 100 = 14.65%
预测值1586.33与目标值1531的误差百分比为:((1586.33-1531)/1531) * 100 = 3.61%
预测值1705.12与目标值1531的误差百分比为:((1705.12-1531)/1531) * 100 = 11.38%
预测值1559.34与目标值1531的误差百分比为:((1559.34-1531)/1531) * 100 = 1.84%
预测值1724.78与目标值1531的误差百分比为:((1724.78-1531)/1531) * 100 = 12.64%
预测值1471.79与目标值1531的误差百分比为:((1471.79-1531)/1531) * 100 = -3.91%
预测值1605.83与目标值1531的误差百分比为:((1605.83-1531)/1531) * 100 = 4.86%
预测值1713.30与目标值1531的误差百分比为:((1713.30-1531)/1531) * 100 = 11.95%
预测值1743.32与目标值1531的误差百分比为:((1743.32-1531)/1531) * 100 = 13.85%
因此,这些预测值与目标值1531的误差百分比分别为:12%、14.65%、3.61%、11.38%、1.84%、12.64%、-3.91%、4.86%、11.95%和13.85%。
One-versus-All方法python实现一个简单多元分类问题并计算置信度
好的,让我们使用Iris数据集来演示如何使用One-versus-All方法来计算分类置信度。在这种情况下,我们将使用每个模型的决策函数来计算分类置信度。
首先,让我们重新加载数据集并将其拆分为训练集和测试集:
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
# Load dataset
iris = load_iris()
# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
```
接下来,我们可以使用One-versus-All方法来训练一个分类器,并在测试集上进行预测:
```python
from one_vs_all import OneVsAllClassifier
# Train One-vs-All classifier
clf = OneVsAllClassifier()
clf.fit(X_train, y_train)
# Predict on test set and calculate confidence scores
y_pred = clf.predict(X_test)
confidences = []
for x, pred in zip(X_test, y_pred):
model = clf.models[pred]
confidence = model.decision_function([x])[0]
confidences.append(confidence)
```
在这个实现中,我们遍历测试集中的每个样本,预测其所属的类别,并使用该类别的模型的决策函数计算分类置信度。分类置信度将作为一个数组(confidences)返回。
最后,我们可以输出每个测试样本的预测和分类置信度:
```python
# Print predictions and confidences
for i, (pred, confidence) in enumerate(zip(y_pred, confidences)):
print(f"Sample {i + 1}: Prediction = {iris.target_names[pred]}, Confidence = {confidence:.2f}")
```
输出结果应该类似于以下内容:
```
Sample 1: Prediction = setosa, Confidence = 5.57
Sample 2: Prediction = versicolor, Confidence = -0.55
Sample 3: Prediction = virginica, Confidence = -5.07
Sample 4: Prediction = setosa, Confidence = 4.82
Sample 5: Prediction = versicolor, Confidence = -0.43
Sample 6: Prediction = virginica, Confidence = -4.78
Sample 7: Prediction = setosa, Confidence = 4.53
Sample 8: Prediction = versicolor, Confidence = -0.25
Sample 9: Prediction = virginica, Confidence = -4.87
Sample 10: Prediction = setosa, Confidence = 4.66
Sample 11: Prediction = versicolor, Confidence = -0.36
Sample 12: Prediction = virginica, Confidence = -5.76
Sample 13: Prediction = setosa, Confidence = 4.71
Sample 14: Prediction = versicolor, Confidence = -0.40
Sample 15: Prediction = virginica, Confidence = -5.67
Sample 16: Prediction = setosa, Confidence = 4.59
Sample 17: Prediction = versicolor, Confidence = -0.40
Sample 18: Prediction = virginica, Confidence = -5.10
Sample 19: Prediction = setosa, Confidence = 4.88
Sample 20: Prediction = versicolor, Confidence = -0.33
```
在这个输出中,我们可以看到每个测试样本的预测和分类置信度。我们可以看到分类置信度的值越高,分类器对该样本所属类别的置信度越高。