Added Principal Component Analysis #9610
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jhaji12
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tianyizheng02
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| """ | ||
| Principal Component Analysis (PCA) is an unsupervised learning | ||
| algorithm that is used for the dimensionality reduction in machine | ||
| learning. It is a statistical procedure that uses an orthogonal | ||
| transformation to convert a set of observations of possibly correlated | ||
| variables into a set of values of linearly uncorrelated variables called | ||
| principal components. | ||
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| Data: The data used for PCA is a set of 500 data points, each with 4 | ||
| features. The data is assumed to be in normal form. | ||
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| Reference: https://en.wikipedia.org/wiki/Principal_component_analysis | ||
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| """ |
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Suggested change
| """ | |
| Principal Component Analysis (PCA) is an unsupervised learning | |
| algorithm that is used for the dimensionality reduction in machine | |
| learning. It is a statistical procedure that uses an orthogonal | |
| transformation to convert a set of observations of possibly correlated | |
| variables into a set of values of linearly uncorrelated variables called | |
| principal components. | |
| Data: The data used for PCA is a set of 500 data points, each with 4 | |
| features. The data is assumed to be in normal form. | |
| Reference: https://en.wikipedia.org/wiki/Principal_component_analysis | |
| """ | |
| """ | |
| Principal Component Analysis (PCA) is an unsupervised learning | |
| algorithm that is used for the dimensionality reduction in machine | |
| learning. It is a statistical procedure that uses an orthogonal | |
| transformation to convert a set of observations of possibly correlated | |
| variables into a set of values of linearly uncorrelated variables called | |
| principal components. | |
| Data: The data used for PCA is a set of 500 data points, each with 4 | |
| features. The data is assumed to be in normal form. | |
| Reference: https://en.wikipedia.org/wiki/Principal_component_analysis | |
| """ |
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| vector = vector - self.mean | ||
| return np.dot(vector, np.transpose(self.components)) |
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| vector = vector - self.mean | |
| return np.dot(vector, np.transpose(self.components)) | |
| vector -= self.mean | |
| return np.dot(vector, np.transpose(self.components)) |
| >>> test_pca.fit(test_data) | ||
| """ | ||
| self.mean = np.mean(vector, axis=0) | ||
| vector = vector - self.mean |
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| vector = vector - self.mean | |
| vector -= self.mean |
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| eigen_vector, eigen_value = np.linalg.eig(cov) | ||
| eigen_vector = eigen_vector.T | ||
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| indexes = np.argsort(eigen_value)[::-1] | ||
| eigen_vector = eigen_vector[indexes] | ||
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| self.components = eigen_vector[: self.n] |
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| eigen_vector, eigen_value = np.linalg.eig(cov) | |
| eigen_vector = eigen_vector.T | |
| indexes = np.argsort(eigen_value)[::-1] | |
| eigen_vector = eigen_vector[indexes] | |
| self.components = eigen_vector[: self.n] | |
| eigenvector, eigenvalue = np.linalg.eig(cov) | |
| eigenvector = eigenvector.T | |
| indexes = np.argsort(eigenvalue)[::-1] | |
| eigenvector = eigenvector[indexes] | |
| self.components = eigenvector[:self.n] |
Nitpick: "eigenvector" and "eigenvalue" are each one word (no spaces)
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