回答編集履歴
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追記
test
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@@ -11,3 +11,69 @@
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[sklearn.metrics.r2_score — scikit-learn 0.19.2 documentation](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html)
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### 追記
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やってみました。
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```python
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import numpy as np
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from scipy import stats
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from scipy.optimize import curve_fit
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import matplotlib.pyplot as plt
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def cdf(x, a, b, c):
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return stats.lognorm.cdf(x, a, b, c)
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def main():
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# xとyが実際に観測されたデータ
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# ここでは擬似データとしてs=1,loc=2,scale=3のscipy.stats.lognorm.cdfを作る。ノイズを適当に加算している
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x = np.linspace(0, 100, 200)
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y = stats.lognorm.cdf(x, 1,2,3) + np.random.normal(size=x.shape)*0.02
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param, cov = curve_fit(cdf, x, y) # 推定
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y_true = cdf(x, 1, 2, 3)
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y_pred = cdf(x, *param)
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print(param) # => [0.96029726 1.90694707 3.08647047] くらい
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plt.plot(x, y, label="観測値")
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plt.plot(x, y_true, label="真値")
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plt.plot(x, y_pred, linestyle="-.", label="フィッティング")
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plt.legend()
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plt.show()
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if __name__ == "__main__":
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main()
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```
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