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def apply_pca (X, standardize=True): # Standardize if standardize: X = (X - X.mean(axis=0)) / X.std(axis=0) # Create principal components pca = PCA () X_pca = pca.fit_transform(X) # Convert to dataframe component_names = [f"PC{i+1}" for i in range (X_pca.shape [1])] X_pca = pd.DataFrame (X_pca, columns=component_names) # Create loadings loadings = pd. DataFrame( pca.components_.T, # transpose the matrix of loadings columns=component_names, # so the columns are the principal components index=X.columns, # and the rows are the original features ) return pca, X_pca, loadings def plot_variance(pca, width=8, dpi=100): # Create figure fig, axs = plt.subplots (1, 2) n = pca.n_components_ grid = np.arange(1, n + 1) # Explained variance evrpca.explained_variance_ratio_ axs[0].bar (grid, evr) axs[0].set( xlabel="Component", title="% Explained Variance", ylim-(0.0, 1.0) ) # Cumulative Variance cv = np.cumsum (evr) axs[1].plot(np.r_[0, grid], np.r_[0, cv], "o-") axs[1].set( xlabel="Component", title="% Cumulative Variance", ylim-(0.0, 1.0) ) # Set up figure fig.set(figwidth=8, dpi=100) return axs

def apply_pca (X, standardize=True): # Standardize if standardize: X = (X - X.mean(axis=0)) / X.std(axis=0) # Create principal components pca = PCA () X_pca = pca.fit_transform(X) # Convert to dataframe component_names = [f"PC{i+1}" for i in range (X_pca.shape [1])] X_pca = pd.DataFrame (X_pca, columns=component_names) # Create loadings loadings = pd. DataFrame( pca.components_.T, # transpose the matrix of loadings columns=component_names, # so the columns are the principal components index=X.columns, # and the rows are the original features ) return pca, X_pca, loadings def plot_variance(pca, width=8, dpi=100): # Create figure fig, axs = plt.subplots (1, 2) n = pca.n_components_ grid = np.arange(1, n + 1) # Explained variance evrpca.explained_variance_ratio_ axs[0].bar (grid, evr) axs[0].set( xlabel="Component", title="% Explained Variance", ylim-(0.0, 1.0) ) # Cumulative Variance cv = np.cumsum (evr) axs[1].plot(np.r_[0, grid], np.r_[0, cv], "o-") axs[1].set( xlabel="Component", title="% Cumulative Variance", ylim-(0.0, 1.0) ) # Set up figure fig.set(figwidth=8, dpi=100) return axs

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Please help explain this code snippet

in the second function, why are we taking transpose.

also ax[1] plot is confusing. please explain this clearly and maybe with example

def apply_pca (X, standardize=True): # Standardize if standardize: X = (X X.mean(axis=0)) / X.std (axis=0) # Create principal components pca = PCA () X_pca = pca.fit_transform(X) # Convert to dataframe component_names = [f"PC{i+1}" for i in range (X_pca.shape [1])] X_pca = pd.DataFrame (X_pca, columns=component_names) # Create loadings loadings = pd. DataFrame ( pca.components_.T, # transpose the matrix of loadings columns=component_names, # so the columns are the principal components index=X.columns, # and the rows are the original features ) return pca, X_pca, loadings def plot_variance (pca, width=8, dpi=100): # Create figure fig, axs = plt.subplots (1, 2) n = pca.n_components_ grid = np.arange(1, n + 1) # Explained variance evrpca.explained_variance_ratio_ axs[0].bar (grid, evr) axs[0].set( xlabel="Component", title="% Explained Variance", ylim=(0.0, 1.0) ) # Cumulative Variance cv = np.cumsum(evr) axs[1].plot(np.r_[0, grid], np.r_[0, cv], "o-") xlabel="Component", title="% Cumulative Variance", ylim-(0.0, 1.0) ) # Set up figure fig. set (figwidth=8, dpi=100) return axs axs[1].set(
Transcribed Image Text:def apply_pca (X, standardize=True): # Standardize if standardize: X = (X X.mean(axis=0)) / X.std (axis=0) # Create principal components pca = PCA () X_pca = pca.fit_transform(X) # Convert to dataframe component_names = [f"PC{i+1}" for i in range (X_pca.shape [1])] X_pca = pd.DataFrame (X_pca, columns=component_names) # Create loadings loadings = pd. DataFrame ( pca.components_.T, # transpose the matrix of loadings columns=component_names, # so the columns are the principal components index=X.columns, # and the rows are the original features ) return pca, X_pca, loadings def plot_variance (pca, width=8, dpi=100): # Create figure fig, axs = plt.subplots (1, 2) n = pca.n_components_ grid = np.arange(1, n + 1) # Explained variance evrpca.explained_variance_ratio_ axs[0].bar (grid, evr) axs[0].set( xlabel="Component", title="% Explained Variance", ylim=(0.0, 1.0) ) # Cumulative Variance cv = np.cumsum(evr) axs[1].plot(np.r_[0, grid], np.r_[0, cv], "o-") xlabel="Component", title="% Cumulative Variance", ylim-(0.0, 1.0) ) # Set up figure fig. set (figwidth=8, dpi=100) return axs axs[1].set(
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