Euclidean
d(x,y)=∑i=1n(xi−yi)2d(\mathbf{x}, \mathbf{y}) = \sqrt{ \sum_{i=1}^{n} (x_i - y_i)^2 }d(x,y)=∑i=1n(xi−yi)2
Cosine
cosine_similarity(x,y)=x⋅y∥x∥ ∥y∥\text{cosine\_similarity}(\mathbf{x}, \mathbf{y}) = \frac{\mathbf{x} \cdot \mathbf{y}}{\|\mathbf{x}\| \, \|\mathbf{y}\|}cosine_similarity(x,y)=∥x∥∥y∥x⋅y
Manhattan
d(x,y)=∑i=1n∣xi−yi∣d(\mathbf{x}, \mathbf{y}) = \sum_{i=1}^{n} |x_i - y_i|d(x,y)=∑i=1n∣xi−yi∣
Last updated 28 days ago
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