ggplot(data = filter(starwars, species != "Hutt")) + geom_point(aes(height, mass, facet_wrap(~ gender) feminine masculine 160 - 120 - gender • feminine 80 - masculine 40 - 100 250 height 150 200 100 150 200 250 mass
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- bool add(int anInt)// Pre: contains(anInt) ? size() <= MAX_SIZE : size() < MAX_SIZE// Post: If contains(anInt) returns false, anInt has been// added to the invoking IntSet as a new element and// true is returned, otherwise the invoking IntSet is// unchanged and false is returned.// bool remove(int anInt)// Pre: (none)// Post: If contains(anInt) returns true, anInt has been// removed from the invoking IntSet and true is// returned, otherwise the invoking IntSet is unchanged// and false is returned. bool add(int anInt); bool remove(int anInt); bool IntSet::add(int anInt){ if(used <= data[MAX_SIZE]) // check if the array is not full { return contains(used); //if not full then return to data } return false;} bool IntSet::remove(int anInt){ cout << "remove() is not implemented yet..." << endl; return false; // dummy value returned}import numpy as np import json img_codes = np.load("data/image_codes.npy") captions = json.load(open('data/captions_tokenized.json')) for img_i in range(len(captions)): for caption_i inrange(len(captions[img_i])): sentence = captions[img_i][caption_i] captions[img_i][caption_i] = ["#START#"] + sentence.split(' ') + ["#END#"] def compute_loss(network, image_vectors, captions_ix): """ :param image_vectors: torch tensor containing inception vectors. shape: [batch, cnn_feature_size] :param captions_ix: torch tensor containing captions as matrix. shape: [batch, word_i]. padded with pad_ix :returns: crossentropy (neg llh) loss for next captions_ix given previous ones. Scalar float tensor """ # captions for input - all except last because we don't know next token for last one. captions_ix_inp = captions_ix[:, :-1].contiguous() captions_ix_next = captions_ix[:, 1:].contiguous() # apply the network, get predictions for captions_ix_next logits_for_next =…import numpy as np import json img_codes = np.load("data/image_codes.npy") captions = json.load(open('data/captions_tokenized.json')) for img_i in range(len(captions)): for caption_i inrange(len(captions[img_i])): sentence = captions[img_i][caption_i] captions[img_i][caption_i] = ["#START#"] + sentence.split(' ') + ["#END#"] class CaptionNet(nn.Module): def__init__(self, n_tokens=n_tokens, emb_size=128, lstm_units=256, cnn_feature_size=2048): """ A recurrent 'head' network for image captioning. See scheme above. """ super().__init__() # a layer that converts conv features to initial_h (h_0) and initial_c (c_0) self.cnn_to_h0 = nn.Linear(cnn_feature_size, lstm_units) self.cnn_to_c0 = nn.Linear(cnn_feature_size, lstm_units) # create embedding for input words. Use the parameters (e.g. emb_size). # YOUR CODE HERE # lstm: create a recurrent core of your network. Use either LSTMCell or just LSTM. # In the latter case (nn.LSTM), make sure batch_first=True # YOUR CODE HERE…
- import numpy as np import json img_codes = np.load("data/image_codes.npy") captions = json.load(open('data/captions_tokenized.json')) for img_i in range(len(captions)): for caption_i inrange(len(captions[img_i])): sentence = captions[img_i][caption_i] captions[img_i][caption_i] = ["#START#"] + sentence.split(' ') + ["#END#"] network = CaptionNet(n_tokens).to(DEVICE) optimizer = torch.optim.Adam(network.parameters(), lr=1e-3) batch_size = 128 n_epochs = 100 n_batches_per_epoch = 50 n_validation_batches = 5 for epoch in range(n_epochs): train_loss = 0 network.train() for _ in tqdm(range(n_batches_per_epoch)): images, captions = generate_batch(train_img_codes, train_captions, batch_size) images = images.to(DEVICE) captions = captions.to(DEVICE) loss_t = compute_loss(network, images, captions) # clear old gradients; do a backward pass to get new gradients; then train with opt # YOUR CODE HERE train_loss += loss_t.detach().cpu().numpy() train_loss /=…What function does this Syntax perform? function myMap() {var map Canvas = document.getElementById("map");var mapOptions = {center: new google.maps.LatLng(51.5, -0.2),zoom: 10};var map = new google.maps.Map(mapCanvas, mapOptions);}PLEASE REFER TO THE IMAGES FOR INSTRUCTIONS PLEASE USE STARTER CODE - CODE IN PYTHON3 ### starter code import random def spider_web(web_map, starting_place, destination): pass def spider_web_rec(web_map, starting_place, destination, visited): pass def make_spider_web(num_nodes, seed=0): if seed: random.seed(seed) web_map = {} for i in range(1, num_nodes + 1): web_map[f'Node {i}'] = [] for i in range(1, num_nodes + 1): sample = random.sample(list(range(i, num_nodes + 1)), random.randint(1, num_nodes - i + 1)) print('sample', i, sample) for x in sample: if i != x: web_map[f'Node {i}'].append(f'Node {x}') web_map[f'Node {x}'].append(f'Node {i}') return web_map if __name__ == '__main__': num_nodes, seed = [int(x) for x in input('Input num_nodes, seed: ').split(',')] the_web = make_spider_web(num_nodes, seed) print(spider_web(the_web, 'Node 1',…
- class Node { public: Node() : data(0), prev(nullptr), next(nullptr) {} Node(int theData, Node* prevLink, Node* nextLink) : data(theData), prev(prevLink), next(nextLink) {} int getData() const { return data; } Node* getPrev() const { return prev; } Node* getNext() const { return next; } void setData(int theData) { data = theData; } void setPrev(Node* prevLink) { prev = prevLink; } void setNext(Node* nextLink) { next = nextLink; } ~Node(){} private: int data; Node* prev; Node* next; }; class AnyList { // friend function overloads the insertion operation public: // copy constructor // overloaded assignment operator // other member functions not necessary for your implementation private: Node *first; Node *last; int count; }; please help write copy constructor #include "AnyList.h" #include <iostream> using namespace std; // Definition of copy constructor // Instructions omitted intentionally. // Your codeFor any element in keysList with a value greater than 100, print the corresponding value in itemsList, followed by a space. Ex: If keysList = {42, 105, 101, 100} and itemsList = {10, 20, 30, 40}, print:20 30 Since keysList.at(1) and keysList.at(2) have values greater than 100, the value of itemsList.at(1) and itemsList.at(2) are printed. #include <iostream>#include <vector>using namespace std; int main() { const int SIZE_LIST = 4; vector<int> keysList(SIZE_LIST); vector<int> itemsList(SIZE_LIST); unsigned int i; for (i = 0; i < keysList.size(); ++i) { cin >> keysList.at(i); } for (i = 0; i < itemsList.size(); ++i) { cin >> itemsList.at(i); } /* Your solution goes here */ cout << endl; return 0;}For any element in keysList with a value greater than 100, print the corresponding value in itemsList, followed by a space. Ex: If keysList = {42, 105, 101, 100} and itemsList = {10, 20, 30, 40}, print: 20 30 Since keysList.at(1) and keysList.at(2) have values greater than 100, the value of itemsList.at(1) and itemsList.at(2) are printed. #include <iostream>#include <vector>using namespace std; int main() {const int SIZE_LIST = 4;vector<int> keysList(SIZE_LIST);vector<int> itemsList(SIZE_LIST);unsigned int i; for (i = 0; i < keysList.size(); ++i) {cin >> keysList.at(i);} for (i = 0; i < itemsList.size(); ++i) {cin >> itemsList.at(i);} /* Your solution goes here */ cout << endl; return 0;} Please help me with this problem using c++.
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