Ddedalus

def progress_bar(current, total, what):
	print('\r', current, "out of", total, what, end="", flush=True)

Ddedalus

def decisionTreeLearning(examples, attributes, parent_examples):
  if len(examples) == 0:
    return pluralityValue(parent_examples)
    # return most probable answer as there is no training data left
  elif len(attributes) == 0:
    return pluralityValue(examples)
  elif (all examples classify the same):
    return their classification
  
  A = max(attributes, key(a)=importance(a, examples)

Ddedalus

zapsmall(matrix)

Ddedalus

matplot(x, y_matrix_of_columns)

Ddedalus

graphWithDensity <- function(no_vertices, dens){
  erdos.renyi.game(no_vertices, as.integer(no_vertices * dens), type="gnm",
  directed = T) # Create a graph with given number of vertices and edges
}
g3 <- graphWithDensity(15, 2)
plot(g3)

Ddedalus

library(fitdistrplus)
library(lattice)
in_geom_fit <- fitdist(in_deg, "geom")
summary(in_geom_fit)
plot(in_geom_fit)
quantiles <- seq(0, 1, 0.05)
qqmath(~in_deg, distribution=function(p){ qgeom(p, prob = in_geom_fit$estimate)}, main="Q-Q plot against geometric distribution", xlab = "geometirc from estimate")

Ddedalus

{plot(density(in_deg), col=2) # where in_deg is a list of degrees - one obs. per verticle
 lines(density(out_deg), col=3)}

Ddedalus

which_mutual(g5) %>% table
which_multiple(g5) %>% table
which_loop(g5) %>% table

Ddedalus

AssignAddin <- function() {
  rstudioapi::insertText(" <- ")
}

Ddedalus

```{r}
plot(degree(g5, mode = "in"), degree(n5, mode = "out"), type = "p")
cor.test(degree(g5, mode = "in"), degree(g5, mode = "out"))
```