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from scipy.stats import gamma, poisson
import numpy as np

# Define the prior parameters for the gamma distribution (alpha and beta)
alpha_prior = 1
beta_prior = 1

# Calculate the posterior parameters
# For gamma-poisson conjugate, the posterior alpha is prior alpha + sum of observed counts
# And posterior beta is prior beta + number of observations

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#日毎の販売回数の集計
# Convert the 'DateTime' column to datetime objects
bakery_data['DateTime'] = pd.to_datetime(bakery_data['DateTime'])

# Extract the date from the 'DateTime' column
bakery_data['Date'] = bakery_data['DateTime'].dt.date

# Group by the date and count the number of transactions per day
daily_sales = bakery_data.groupby('Date')['TransactionNo'].nunique()

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from sklearn.preprocessing import MinMaxScaler

# Dropping the 'Cluster' column for scaling
cluster_data_for_scaling = cluster_means_with_diabetes_rate.drop('Cluster', axis=1)

# Applying Min-Max scaling
scaler = MinMaxScaler()
cluster_scaled = scaler.fit_transform(cluster_data_for_scaling)

# Creating a DataFrame from the scaled data

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# Full Python code to perform clustering and calculate statistics
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns

# Assuming 'data' is the original DataFrame and 'features' are the feature columns

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from mpl_toolkits.mplot3d import Axes3D

def biplot_3d_with_arrows(score, coeff, diabetes_data, ax):
    xs = score[:,0]
    ys = score[:,1]
    zs = score[:,2]
    n = coeff.shape[0]

    # Plotting points for diabetes and non-diabetes cases
    ax.scatter(score[diabetes_data == 1, 0], score[diabetes_data == 1, 1], score[diabetes_data == 1, 2], color='red', s=5, label='Diabetes')

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def biplot_non_diabetes_only(score, coeff, diabetes_data):
    xs = score[:,0]
    ys = score[:,1]
    n = coeff.shape[0]
    scalex = 1.0/(xs.max() - xs.min())
    scaley = 1.0/(ys.max() - ys.min())

    # Plotting points for non-diabetes cases only
    non_diabetes_points = score[diabetes_data == 0]
    plt.scatter(non_diabetes_points[:, 0] * scalex, non_diabetes_points[:, 1] * scaley, color='blue', s=5, label='Non-Diabetes')

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def biplot_non_diabetes_only(score, coeff, diabetes_data):
    xs = score[:,0]
    ys = score[:,1]
    n = coeff.shape[0]
    scalex = 1.0/(xs.max() - xs.min())
    scaley = 1.0/(ys.max() - ys.min())

    # Plotting points for non-diabetes cases only
    non_diabetes_points = score[diabetes_data == 0]
    plt.scatter(non_diabetes_points[:, 0] * scalex, non_diabetes_points[:, 1] * scaley, color='blue', s=5, label='Non-Diabetes')

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def biplot_diabetes_smaller_arrows(score, coeff, diabetes_data):
    xs = score[:,0]
    ys = score[:,1]
    n = coeff.shape[0]
    scalex = 1.0/(xs.max() - xs.min())
    scaley = 1.0/(ys.max() - ys.min())

    # Plotting points for diabetes cases only
    diabetes_points = score[diabetes_data == 1]
    plt.scatter(diabetes_points[:, 0] * scalex, diabetes_points[:, 1] * scaley, color='red', s=5, label='Diabetes')

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# Creating a plot for the cumulative variance with a horizontal line at 80%
plt.figure(figsize=(10, 6))
plt.plot(cumulative_variance, marker='o')

# Adding annotations for each point
for i, v in enumerate(cumulative_variance):
    plt.text(i, v + 0.01, f"{v:.2f}", ha='center', va='bottom', fontsize=8)

# Adding a horizontal line at 80% cumulative variance
plt.axhline(y=0.80, color='r', linestyle='--')

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import matplotlib.pyplot as plt
import seaborn as sns

# Creating a heatmap for the loadings
plt.figure(figsize=(14, 8))
sns.heatmap(loadings_df_80_transposed, cmap='coolwarm', annot=True)
plt.title('Heatmap of PCA Loadings')
plt.ylabel('Principal Components')
plt.xlabel('Features')
plt.show()