AayushSameerShah

Many times you want to write the formulae in which the limits need to be written up side and low side of the sigma. But the problem happens when it is not written as expected.

In such times, just add \displaystyle brfore the sum:

The problem:

$$\varrho(\tau) = \frac{ \sum_{t=1}^{T-\tau} (y_t - \mu) (y_{t + \tau} - \mu)} {(T - \tau)\varrho^2}$$

The solution

$$\varrho(\tau) = \frac{\displaystyle \sum_{t=1}^{T-\tau} (y_t - \mu) (y_{t + \tau} - \mu)} {(T - \tau)\varrho^2}$$

AayushSameerShah

// suppose we have the list like this (not to run but just idea)
List<List<Integer>> data = [
  [1, 2, 3],
  [2, 3, 4],
  [3, 4, 5]
];

// Now to convert each List<Integer> to Row so that can be used to make DF
List<Row> rows = new ArrayList<>();
for (List<Integer> that_line : data){

AayushSameerShah

def relative_root_mean_squared_error(true, pred):
    n = len(true) # update
    num = np.sum(np.square(true - pred)) / n  # update
    den = np.sum(np.square(pred))
    squared_error = num/den
    rrmse_loss = np.sqrt(squared_error)
    return rrmse_loss

AayushSameerShah

#pip install misspy for installation
from missingpy import MissForest
imputer = MissForest()
X_imputed = imputer.fit_transform(X)

AayushSameerShah

import static org.apache.spark.sql.functions.*;

result = result.withColumn("row_index", row_number().over(Window.orderBy(monotonically_increasing_id())));
DF = DF.withColumn("row_index", row_number().over(Window.orderBy(monotonically_increasing_id())));

result = result.join(DF.withColumn("realBMI",col("BMI")).select("row_index", "realBMI"), result.col("row_index").equalTo(DF.col("row_index")));
result = result.drop("row_index");
result.show();

AayushSameerShah

def get_outlier_indices(columns_list):
    indices = []
    for column in columns_list:
        feature = df[column]
        Q1, Q3 = feature.quantile([0.25, 0.75]).values
        IQR = Q3 - Q1
        lower = Q1 - (1.5 * IQR)
        upper = Q3 + (1.5 * IQR)
        
        inds = list(df[(df[column] < lower) | (df[column] > upper)].index.values)

AayushSameerShah

# Need to make axes
ax = plt.axes()
# Bar plot with sorted index (where the bars will be like |li|liii| ← unsorted
df.value_counts().plot.bar(ax=ax)

# Store individual heights
heights = []
for patch in ax.patches:
    heights.append(patch.get_height())
heights = np.array(heights)

AayushSameerShah

Here, I will talk about the triggers. As I have learnt triggers way back when I was doing my graduation, it seem like a boring or more complex stuff... but now as I can understand it, it is pretty pretty simple and easy stuff to implement and to understand.

What it is?

It is a way to automatically trigger a query (run a query) when certain event has happened. Like, if I am adding a new user, then automatically new table should be created, or let's

AayushSameerShah

This is the data...

Title	Author	Pages
A man from sky	Aayush	334
I know, what miracle is.	Aayush	3134
album001	Sameer	134
How to feel happiness?	Sameer	534
The secrets of woderland	Sameer	834
An Adventurous Guy	Baa	1234

AayushSameerShah

This time, I am gonna write the small, new things which I learnt before but by the time forgot them. Following the course on MySQL I will be noting down the stuff. As you might have noticed it is in the gist format, so the notes will not be much comprehensive, but they will be in such a form that you can refer back again. Like a cheat sheet!

See which database you are in?

SELECT database();

See columns of a table with query!

SHOW COLUMNS FROM table_name;