วีดีโอ

Good one 🎉 import pygad import numpy as np # Input variables X = [4, -2, 3.5, 5, -11, -4.7] desired_output = 44 # Define the fitness function def fitness_function(ga_instance, solution, solution_idx): output = np.sum(solution * X) fitness = 1.0 / (np.abs(output - desired_output)) # Adding a small constant to avoid division by zero return fitness # Create an instance of the genetic algorithm ga_instance = pygad.GA(num_generations=50,#The algorithm will run for 50 generations. num_parents_mating=8,#Eight parents are selected for mating in each generation. fitness_func=fitness_function, sol_per_pop=8,#The population contains 8 solutions (sets of weights). num_genes=len(X),#The number of genes per solution is equal to the number of elements in X init_range_low=-2,#The initial range for genes (weights) is set between -2 and 5. init_range_high=5,#range of weight parent_selection_type="sss",#Uses Steady-State Selection to maintain diversity. keep_parents=1,# At least one parent solution from each generation is retained in the next generation. crossover_type="single_point",#Single-point crossover for genetic recombination. mutation_type="random",# Each child has 10% of its genes randomly mutated. mutation_percent_genes=10 # perecentage in genes ) # Run the genetic algorithm ga_instance.run() # Get the best solution solution, solution_fitness, solution_idx = ga_instance.best_solution() print("Solution:", solution) print("Fitness:", solution_fitness) print("Solution Index:", solution_idx) import numpy as np import skfuzzy as fuzz from skfuzzy import control as ctrl # Define fuzzy variables quality = ctrl.Antecedent(np.arange(0, 11, 1), 'quality') service = ctrl.Antecedent(np.arange(0, 11, 1), 'service') tip = ctrl.Consequent(np.arange(0, 26, 1), 'tip') # Automatically create membership functions for quality and service quality.automf(3) # poor, average, good service.automf(3) # poor, average, good # Custom membership functions for tip tip['low'] = fuzz.trimf(tip.universe, [0, 0, 13]) tip['medium'] = fuzz.trimf(tip.universe, [0, 13, 25]) tip['high'] = fuzz.trimf(tip.universe, [13, 25, 25]) # Define rules rule_1 = ctrl.Rule(quality['poor'] | service['poor'], tip['low']) rule_2 = ctrl.Rule(quality['average'] | service['average'], tip['medium']) rule_3 = ctrl.Rule(quality['good'] | service['good'], tip['high']) # Create control system and simulation tipping_ctrl = ctrl.ControlSystem([rule_1, rule_2, rule_3]) tipping = ctrl.ControlSystemSimulation(tipping_ctrl) # Input values tipping.input['quality'] = 6.5 tipping.input['service'] = 5.5 # Compute result tipping.compute() # Output result print("Recommended tip:", tipping.output['tip']) num_ip = int(input("Enter the number of inputs: ")) theta = 1 x1 = [] x2 = [] # Collect inputs for x1 and x2 for i in range(num_ip): a = int(input(f"Enter the input for x1[{i+1}]: ")) x1.append(a) b = int(input(f"Enter the input for x2[{i+1}]: ")) x2.append(b) # Display the input lists print('x1:', x1) print('x2:', x2) # Case 1: w1 and w2 = 1 print("Case 1: w1 and w2 = 1") w1 = w2 = 1 case_y1 = [] case_yin1 = [] # Calculate net input (yin) and output (y) for Case 1 for i in range(num_ip): yin = (x1[i] * w1) + (x2[i] * w2) case_yin1.append(yin) case_y1.append(1 if yin >= theta else 0) # Display results for Case 1 print('Net Inputs (Case 1):', case_yin1) print('Outputs (Case 1):', case_y1) # Case 2: w1 = 1 and w2 = -1 print("Case 2: w1 = 1 and w2 = -1") w1 = 1 w2 = -1 case_y2 = [] case_yin2 = [] # Calculate net input (yin) and output (y) for Case 2 for i in range(num_ip): yin = (x1[i] * w1) + (x2[i] * w2) case_yin2.append(yin) case_y2.append(1 if yin >= theta else 0) # Display results for Case 2 print('Net Inputs (Case 2):', case_yin2) print('Outputs (Case 2):', case_y2) from sklearn.datasets import make_classification from sklearn.linear_model import Perceptron # Generate a classification dataset x, y = make_classification(n_samples=1000, n_features=10, n_redundant=0, random_state=1) # Create and train the Perceptron model model = Perceptron() model.fit(x, y) print(x[0]) # Define a new input row for prediction row = [-0.47269434, 1.58052729, -0.90585307, -1.39894317, 0.63344087, -0.51944996, -0.09536129, -0.11618216, -0.10737825, -0.36789953] # Make a prediction y_pred = model.predict([row]) # Display the prediction print('Predicted:', int(y_pred[0]))

Zabardust......😊😊😊

Aur tum नागपूर के पास का हिल स्टेशन बताना तो भूल गये woh hai चिखलदरा.५) उसके बाद नंदुरबार जीले मै तोरणमाळ हिल स्टेशन हैं जो के abtak kafi vergin हैं ज्यादा लोक जाते नही एकदम peaceful hai..६) संभाजीनगर (औरंगाबाद) यहापे भी म्हैसमाळ नाम का छोटासा लेकीन बहोत प्यारासा हिल स्टेशन हैं जो के कन्नड घाट मे स्थिथ हे साथ ही yehase आप अजंता aur वेरूळ caves aur देवगिरी फोर्ट aur Grishneshwar ज्योतिर्लिंग जा sakte hai ६) नासिक शहर itself ek hill statoin jaisa hi hai iske aspas सप्तश्रृंगी शक्तीपीठ,trimbakeshwar jyotirling aur बहोत से इंटरेस्टिंग places हैं जैसे की इगतपुरी भंडारदरा सापुतारा etc. To भाई माना के tum maharashtrian nahi हो पर आधी अधुरी aur galat jankari देकर khudka mazak na banaoo.. जय महाराष्ट्र

भाई लागता है tumhari knowledge bahot bekar hai kyon batata हून १) लोणावळा खंडाळा एक ही हिल station hai alag se nahi Mane jate aur राजमाची ,लोहगड, विसापूर ये आसपास के ॲडव्हेंचर forts.२) पांचगणी aur mahabaleshwar bhi ek hi twin हिल स्टेशन हैं ise bhi alag से nahi ginana हैं aur तुमने kaha ki ye हिल स्टेशन १३७२ फीट उंचा है sea level se to बिलकुल galat ye १३७२ फीट नही मीटर ऊंचा हैं aur maharashtra ka second highest peak hai.३) भंडारदरा yahape रंधा धबधबा यानी फॉल्स aur arther lake dam hai sath hi maharashtra ka highest peak कळसूबाई yahi hai jo shayad तुम्हे maalum nahi ya to batane ki allergy hai.४)tumne भंडारदरा के नजदीक railway station नागपूर bataya are bawle had kar di tumne ek bar Pune ya Mumbai kahte to chalta tha lekin मी बताता हून यहाका सबसे najdiki स्टेशन नासिक है सिटी सिटी

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Chikhaldara most beautiful hill station is missing

Yeh hill stations nahi monsoon stations hai. Aapke bhi jyada tar pics monsoon wali hai

Dekho Bhai aap galat bol rahe ho Matheran ka nazdiki railway station Nerul Nahin Neral hai Nerul to Navi Mumbai mein hai aur Neral alag hai joki Raigarh Mein Aata Hai to Matheran vahi hai

Thanks for the video.

भंडारदरा है वो

Bhai idar weight lifting hota he

Nice bro

Jinko maja 👍😎 aya like karo

Mere ya he bhandardara

Matheran ke liye Jo railway station hai woh nerul nahi neral hai Aap ne nerul bataya

Cheetah dekhne ko milega ... Hey Deva , thoda to pad k video banao ... India me cheetah nahi paya jata Bhai ..

Chikhaldara dekho

Thanks

फार छान आहेत फोटो

sir Amravati Maharashtra k dakshin me nahi hai, Maharashtra k nort-east me hai . Aur Ambadevi mandir dastur nagar me nahi hai

Namaskar Ji Sikh Sardaron ne Kewal 1% jansankhya hone ke bavjud Bharat ki azadi ki LADAI me 65-70% shahidi aur yogdaan diya tha:--- Maut Fansi ki saja Sikh 93 others 28 Aajivan karavas Sikh 2147 others 499 Jalianwala Bagh Shahidi kand Sikh 799 others 501 Bajbaj Ghat Shahidi kand Sikh 67 others 46 Kuka lehar mein shahidi Sikh 91 others------ Akali Lehar mein Shahidi Sikh 500 others--- Jai Hind Jai Bharat

Nice video THANKS

😂🤣😂🤣😂🤣😂🤣😂😂🤣🤣😂🤣😂🤣😂🤣😂🤣😂🤣😂🤣😂🤣😂

ना कोई बडाकारखाना है । नाकोई उदद्योग पाणी की किल्लत है सनसनाटी धूप है । खेती में व्यस्त लेकिन डूबीत किसान ।

I love Amravati

I am from dharni melghat Amravati

Aare Bhai Aapne chikaldara to bola hi nhi

सर आप भी अमरावती मे ही रहते हो क्या सर

bhai is Complex me Running ki jati hai kya Plzz reply

Bhai kuch kuch galti hai

Sabse bakwas gaon

Oyee bhai ambadevi mandir hvpm ke pass hai

I'm from amravati chandni chowk

Bakwas

अबे चूतिये तुने भूगोल की कौनसी किताब में देखा की अमरावती महाराष्ट्र के दक्षिण भाग में है। साला जब से व्हाट्सएप और यूट्यूब आया तब बेवकुब लोग भी वीडियो बनाकर ज्ञान दे रहे है। और जो छोटे बच्चे है। इसी गलत ज्ञान को सही समझेंगे।

❤️

भंडारदरा नागपूर जवळ नाही . मुंबई पुणेजवळ आहे. बाकी माहिती मस्ती.

Ghumne Ka Maza Praivat Four Wihilurs ❤️❤️❤️

Wa wa ky batt hai ambadevi temple dastur nagar me nahi hi

pls add brand factory,wallmart dmart to it,make video on thibs

सेमादोह,गोविलगढ़ किला,यवतमाल ओर नरनाला फोर्ट ग़लत बोला।

अमरावती district hara bhara area bole to मेलघाट hai

Aap ne amravati ke bare me bataya par bahot kuch miss kardiya

Amba devi mandir Gandhi chowk pe hai

भाई साहब अम्बादेवी मंदिर दस्तूर नगर में नही है, ये राजकमल चौक के आगे हनुमान व्यायाम मंडल संस्था को लगके है. अम्बा गेट को लगके है. अम्बा देवी मंदिर और एकवीरा देवी मंदिर भी है आसपास.

I'm from Amravati..and I happy to see this info about Amravati

Nice I like that places 👍

History of aurangabad

Mai amravati se hu lekin is video me kuch baate jhut hai zada mat feko Bas chikhaldar ki bate sahi hai