mport pandas as pd import matplotlib.pyplot as plt dengue = pd.read_excel('Dengue.xlsx', header=0, index_col='Date', parse_dates=True, squeeze=True) reg = dengue['Total'] reg = reg.reset_index() reg.hist(bins = 100) plt.grid(False)
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- 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#"] 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 /=…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…
- FIX THIS CODE Using python Application CODE: import csv playersList = [] with open('Players.csv') as f: rows = csv.DictReader(f) for r in rows: playersList.append(r) teamsList = [] with open('Teams.csv') as f: rows = csv.DictReader(f) for r in rows: teamsList.append(r) plays=len(playersList) for i in range(plays): if playersList[i]['team']=='Argentina' and int(playerlist[i]['minutes played'])<200 and int(playersList[i] ['shots'])>20: print(playersList[i]['last name']) c0=0 c1=0 c2=0 for i in range(len(teamsList)): if int(teamsList[i]['redCards'])==0: c0=c0+1 if int(teamsList[i]['redCards'])==1: c1=c1+1 if int(teamsList[i]['redCards'])==2: c2=c2+1 print("Number of teams with zero redcards:",c0) print("Number of teams with zero redcards:",c1) print("Number of teams with zero redcards:",c2) ratio=0 for i in range(len(teamsList)): if int(teamsList[i]['games']>3) and if int(teamsList[i]['goalsFor'])/int(teamsList[i]['goalsAgainst'])<ratio:…PYTHON import pandas as pdfrom datetime import dateimport sys from sklearn.preprocessing import OrdinalEncoder def series_report( series, is_ordinal=False, is_continuous=False, is_categorical=False): print(f"{series.name}: {series.dtype}") ###### Your code here ###### # Check command line argumentsif len(sys.argv) < 2: print(f"Usage: python3 {sys.argv[0]} <input_file>") exit(1) # Read in the datadf = pd.read_csv( sys.argv[1], index_col="employee_id") # Convert strings to dates for dob and deathdf['dob'] = df['dob'].apply(lambda x: date.fromisoformat(x))df['death'] = df['death'].apply(lambda x: date.fromisoformat(x)) # Show the shape of the dataframe(row_count, col_count) = df.shapeprint(f"*** Basics ***")print(f"Rows: {row_count:,}")print(f"Columns: {col_count}") # Do a report for each columnprint(f"\n*** Columns ***")series_report(df.index, is_ordinal=True)series_report(df["gender"], is_categorical=True)series_report(df["height"], is_ordinal=True,…def get_nearest_station(my_latitude: float, my_longitude: float, stations: List['Station']) -> int: """Return the id of the station from stations that is nearest to the location given by my_latidute and my_longitude. In the case of a tie, return the ID of the last station in stations with that distance. Preconditions: len(stations) > 1 >>> get_nearest_station(43.671134, -79.325164, SAMPLE_STATIONS) 7571 >>> get_nearest_station(43.674312, -79.299221, SAMPLE_STATIONS) 7486 """
- For items 1–3, use the IN300_Dataset1.csv file. Write a Python program that reads the CSV file into a Panda dataframe. Using that dataframe, print the row, source IP, and destination IP as a table. Write a Java program that reads the CSV file into an ArrayList. Convert the ArrayList to a string array and print the row, source IP, and destination IP on the same line using a loop. Write an R program that reads the CVS file using the read.csv data type. Print the row, source IP and destination IP of each line.#The Iris Dataset import sklearn.datasetsimport matplotlib.pyplot as plt import numpy as np import scipy iris = sklearn.datasets.load_iris() Write a function that takes in an index i and prints out a verbose desciption of the species and measurements for data point i. For example:Data point 5 is of the species setosaIts sepal length (cm) is 5.4Its sepal width (cm) is 3.9Its petal length (cm) is 1.7Its petal width (cm) is 0.4DEFAULT_SIZE = (256, 512) import torch class LaneDataset(torch.utils.data.Dataset): def__init__(self, dataset_path, train=True, size=DEFAULT_SIZE): # code here def__getitem__(self, idx): # code here return image, segmentation_image, instance_image # l x H x W [[0, 1], [2, 0]] def__len__(self): # code here The output dimension of the instance segmentation embedding should be equal to 5.
- from typing import List, Tuple, Dict from poetry_constants import (POEM_LINE, POEM, PHONEMES, PRONUNCIATION_DICT, POETRY_FORM_DESCRIPTION) # ===================== Provided Helper Functions ===================== def transform_string(s: str) -> str: """Return a new string based on s in which all letters have been converted to uppercase and punctuation characters have been stripped from both ends. Inner punctuation is left untouched. >>> transform_string('Birthday!!!') 'BIRTHDAY' >>> transform_string('"Quoted?"') 'QUOTED' >>> transform_string('To be? Or not to be?') 'TO BE? OR NOT TO BE' """ punctuation = """!"'`@$%^&_-+={}|\\/,;:.-?)([]<>*#\n\t\r""" result = s.upper().strip(punctuation) return result def is_vowel_phoneme(phoneme: str) -> bool: """Return True if and only if phoneme is a vowel phoneme. That is, whether phoneme ends in a 0, 1, or 2. Precondition:…astfoodStats Assignment Description For this assignment, name your R file fastfoodStats.R For all questions you should load tidyverse, openintro, and lm.beta. You should not need to use any other libraries. suppressPackageStartupMessages(library(tidyverse)) suppressPackageStartupMessages(library(openintro)) suppressPackageStartupMessages(library(lm.beta)) The actual data set is called fastfood. Continue to use %>% for the pipe. CodeGrade does not support the new pipe. Round all float/dbl values to two decimal places. All statistics should be run with variables in the order I state E.g., "Run a regression predicting mileage from mpg, make, and type" would be: lm(mileage ~ mpg + make + type...) To access the fastfood data, run the following: fastfood <- openintro::fastfood Create a correlation matrix for the relations between calories, total_fat, sugar, and calcium for all items at Sonic, Subway, and Taco Bell, omitting missing values with na.omit(). Assign the…How to get the first entry in each raw for csv column in jupyter notebook using python and add it to new column? For example: Documentary from first raw ['Documentary', 'Animation', "Family'] Sample from the column: genre ('Documentary', 'Animation', 'Family') [Crime', 'Horror', Thriller'] ['Family'] ('Documentary'] [Thriller'] ['Action', 'Sci-Fi'] ['Documentary', 'Comedy', 'Drama', 'Fantasy', 'Mystery', 'Sci- Fi')