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次㮠recipe ãèªãã åŸãããç°¡åã«åæ§æããŠãã ããã | ã¬ã·ã: ã°ãªãŒã³ãããŒãã¹ã¢ãŒãã³ã°ã¹ã ãŒãžãŒ
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次ã®ããã¹ããç·šéããŠãçºæã®æŠèŠãšãã®å©ç¹ãæ確ã«ããããã«ããŠãã ãã | ãã®æ°ããæ©æ¢°ã¯ãèªåçã«é£æã眮ãããç¿ã«ç§»åããããšãã§ããŸãã | ãã®ææ°ã®èªååé£æé
åžã·ã¹ãã ã¯ãä»»æã®é£æããæŽã濯ããç¿ã«èªåçã«ç§»åãããŸãããã®ã€ãããŒã·ã§ã³ã«ãããå人ãšå®¶æãã¬ã¹ãã©ã³ãã€ã³ã¹ã¿ãŒã·ã§ã³ãªã©ã®å©çšã·ãŒã³ãé£æã®æºåãšæŽçã«å¿
èŠãªæéãšåŽåã倧å¹
ã«åæžã§ããŸãããŸããæäœæ¥ãæå°éã«æããããšã§ãè¡çé¢ã§ã®å©ç¹ããããŸãã |
ãçœããšãç·ããå«ã3è²ã®ååãè¿ãé¢æ°ãäœæããŠãã ããã | 次ã®é¢æ°ã䜿çšããŠããçœããšãç·ããå«ã3è²ã®ååãååŸããŸãïŒ
```
function getColors() {
var colors = ['çœ', 'ç·', 'é', 'èµ€', 'é»'];
var pickedColors = [];
for (var i = 0; i < 3; i++) {
var randomColor = colors[Math.floor(Math.random() * colors.length)];
if (randomColor === 'çœ' || randomColor === 'ç·') {
pickedColors.push(randomColor);
} else {
i--;
}
}
return pickedColors;
}
```
ãã®é¢æ°ã¯ããçœããšãç·ããå«ã5ã€ã®è²ãä¿æããé
åã䜿çšããŸãããã®åŸãthis function picks 3 random colors from the array and checks if they include "white" and "green" if not, it picks another random color until it picks 3 colors that include "white" and "green", This function returns an array of 3 colors that include "white" and "green". |
|
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- [Environmental impact of automobiles](https://www.ucsusa.org/âresources/environmental-impact-automobiles)
- [Air pollution from cars](https://www.epa.gov/air-pollution-transportation)
- [Impacts of car use on the environment](https://www.theguardian.com/environment/2010/aug/03/environmental-impact-cars) |
|
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ãã®python ã³ãŒããæ¥æ¬èªã§èª¬æããŠãã ããã | ```
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def sigmoid_derivative(x):
return sigmoid(x) * (1 - sigmoid(x))
def neural_network(input, weights):
output = sigmoid(np.dot(input, weights))
return output
input = np.array([[1, 1, 1, 0, 0, 0]])
weights = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
output = neural_network(input, weights)
print(output)
``` | ãã®Pythonã³ãŒãã¯ããã¥ãŒã©ã«ãããã¯ãŒã¯ã®åçŽãªäŸã瀺ããŠããŸãã
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