Allgoerithm

id,price,levy,manufacturer,model,production_year,category,leather_interior,fuel_type,engine volume,mileage,cylinders,gear_box_type,drive_wheels,doors,wheel,color,airbags
45654403,13328,1399,LEXUS,RX 450,2010,Jeep,Yes,Hybrid,3.5,186005 km,6.0,Automatic,4x4,04-May,Left wheel,Silver,12
44731507,16621,1018,CHEVROLET,Equinox,2011,Jeep,No,Petrol,3,192000 km,6.0,Tiptronic,4x4,04-May,Left wheel,Black,8
45774419,8467,-,HONDA,FIT,2006,Hatchback,No,Petrol,1.3,200000 km,4.0,Variator,Front,04-May,Right-hand drive,Black,2
45769185,3607,862,FORD,Escape,2011,Jeep,Yes,Hybrid,2.5,168966 km,4.0,Automatic,4x4,04-May,Left wheel,White,0
45809263,11726,446,HONDA,FIT,2014,Hatchback,Yes,Petrol,1.3,91901 km,4.0,Automatic,Front,04-May,Left wheel,Silver,4
45802912,39493,891,HYUNDAI,Santa FE,2016,Jeep,Yes,Diesel,2,160931 km,4.0,Automatic,Front,04-May,Left wheel,White,4
45656768,1803,761,TOYOTA,Prius,2010,Hatchback,Yes,Hybrid,1.8,258909 km,4.0,Automatic,Front,04-May,Left wheel,White,12
45816158,549,751,HYUNDAI,Sonata,2013,Sedan,Yes,Petrol

Allgoerithm

ID,Price,Levy,Manufacturer,Model,Prod. year,Category,Leather interior,Fuel type,Engine volume,Mileage,Cylinders,Gear box type,Drive wheels,Doors,Wheel,Color,Airbags
45654403,13328,1399,LEXUS,RX 450,2010,Jeep,Yes,Hybrid,3.5,186005 km,6.0,Automatic,4x4,04-May,Left wheel,Silver,12
44731507,16621,1018,CHEVROLET,Equinox,2011,Jeep,No,Petrol,3,192000 km,6.0,Tiptronic,4x4,04-May,Left wheel,Black,8
45774419,8467,-,HONDA,FIT,2006,Hatchback,No,Petrol,1.3,200000 km,4.0,Variator,Front,04-May,Right-hand drive,Black,2
45769185,3607,862,FORD,Escape,2011,Jeep,Yes,Hybrid,2.5,168966 km,4.0,Automatic,4x4,04-May,Left wheel,White,0
45809263,11726,446,HONDA,FIT,2014,Hatchback,Yes,Petrol,1.3,91901 km,4.0,Automatic,Front,04-May,Left wheel,Silver,4
45802912,39493,891,HYUNDAI,Santa FE,2016,Jeep,Yes,Diesel,2,160931 km,4.0,Automatic,Front,04-May,Left wheel,White,4
45656768,1803,761,TOYOTA,Prius,2010,Hatchback,Yes,Hybrid,1.8,258909 km,4.0,Automatic,Front,04-May,Left wheel,White,12
45816158,549,751,HYUNDAI,Sonata,2013,Sedan,Yes,Petrol,2.4,

Allgoerithm

ID,Delivery_person_ID,Delivery_person_Age,Delivery_person_Ratings,Restaurant_latitude,Restaurant_longitude,Delivery_location_latitude,Delivery_location_longitude,Type_of_order,Type_of_vehicle,Time_taken(min)
4607,INDORES13DEL02,37,4.9,22.745049,75.892471,22.765049,75.912471,Snack ,motorcycle ,24
B379,BANGRES18DEL02,34,4.5,12.913041,77.683237,13.043041,77.813237,Snack ,scooter ,33
5D6D,BANGRES19DEL01,23,4.4,12.914264,77.6784,12.924264,77.6884,Drinks ,motorcycle ,26
7A6A,COIMBRES13DEL02,38,4.7,11.003669,76.976494,11.053669,77.026494,Buffet ,motorcycle ,21
70A2,CHENRES12DEL01,32,4.6,12.972793,80.249982,13.012793,80.289982,Snack ,scooter ,30
9BB4,HYDRES09DEL03,22,4.8,17.431668,78.408321,17.461668,78.438321,Buffet ,motorcycle ,26
95B4,RANCHIRES15DEL01,33,4.7,23.369746,85.33982,23.479746,85.44982,Meal ,scooter ,40
9EB2,MYSRES15DEL02,35,4.6,12.352058,76.60665,12.482058,76.73665,Meal ,motorcycle ,32
1102,HYDRES05DEL02,22,4.8,17.433809,78.386744,17.563809,78.516744,Buffet ,motorcycle ,34

Allgoerithm

# observed data
total_rock = tf.constant(5., tf.float32) 
total_paper = tf.constant(0., tf.float32)
total_scissors = tf.constant(0., tf.float32)

# define some constants
number_of_steps = 10000
burnin = 5000

# Set the chain's start state

Allgoerithm

# We calculate the joint log probability.
# It answers the big and central question in each Markov chain simulation: What's the probability
# that this data occurs together with these distribution parameters?
@tf.function(input_signature=5 * (tf.TensorSpec(shape=[], dtype=tf.float32),))
def joint_log_prob(total_rock, total_paper, total_scissors, p_rock, p_paper):
    '''
    Joint log probability of data occuring together with given parameters.

    :param total_rock: number of rock occurences
    :param total_paper: number of paper occurences

Allgoerithm

Allgoerithm