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from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

X, y = load_iris()
clf = LogisticRegression(random_state = 0).fit(X,y)
clf.predict(X[:2,:])

clf.predict_proba(X[:2,:])

clf.score(X,y)

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from sklearn.model_selection import train_test_split

# Creating the training and testing data
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.33)

# Initializing an logistic regression object
clf = LogisticRegression()

# Fit the model to training and test sets
clf.fit(X_train,Y_train)

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### Hyper parameter tuning Logistic Regression model

from sklearn.model_selection import GridSearchCV
from sklearn import linear_model
from sklearn.pipeline import make_pipeline

lr = linear_model.LogisticRegression(solver ='lbfgs', penalty = 'l2')

# Use gridsearch CV to search for the bes parameter

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# Scaling the data
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

po = [('scaler',StandardScaler()),('logistic_reg',LogisticRegression(C=10))]
p = Pipeline(po)
lr4 = p.fit(X_train, Y_train)
print("Scaled Accuracy score : ",100*lr4.score(X_test,Y_test),"% \n")

# Logistic Regression coefficients

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import sys, cv2
import matplotlib.pyplot as plt

## Load the image
f = sys.argv[1]
img = cv2.imread('/a.jpeg')

## display the image
plt.imshow(img)
plt.show()

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## Load the image
f3 = sys.argv[1]
img3 = img
## convert image to grayscale and display img
img3_gray = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)
cv2_imshow(img3_gray)
## histogram equalization of color images 
img3c = cv2.cvtColor(img3,cv2.COLOR_BGR2YUV)
## equalize the pixels across y channel and display
img3c[:,:,0] = cv2.equalizeHist(img3c[:,:,0])

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from google.colab.patches import cv2_imshow

## load new image and convert to gray scale
f2 = sys.argv[1]
img2 = cv2.imread('/iPhone_camera_1615895489376.webp',cv2.IMREAD_GRAYSCALE)
## ht, width of image
h, w = img2.shape
## horizontal edge detection
sobel_h = cv2.Sobel(img2, cv2.CV_64F, 1, 0, ksize = 5)
## Vertical edge detection

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